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Workiva Interview Questions

Workiva Interview Guides

Data Structures & Algorithms

Given two sorted lists, write a function to merge them into one sorted list.

Merge Sorted Lists

Compute Deviation

Find the missing integer from a array of consequtive integers

Find the Missing Number

Find Bigrams

Write a function to create a single dataframe with complete addresses in the format of street, city, state, zip code.

Complete Addresses

Select the 2nd highest salary in the engineering department

2nd Highest Salary

Write a SQL query to create a histogram of the number of comments per user in the month of January 2020.

Comments Histogram

Write a query that returns all neighborhoods that have 0 users.

Empty Neighborhoods

Calculate the first touch attribution for each `user_id` that converted.

First Touch Attribution

Closest SAT Scores

Machine Learning

Encoding Categorical Features

Coefficients of Logistic Regression

Booking Regression

Job Recommendation

Say you are given a dataset of perfectly linearly separable data. What would happen when you run logistic regression?

Perfectly Separable

Probability

First to Six

500 Cards

Raining in Seattle

Found Item

Ad Raters

Statistics

P-value to a Layman

Reducing Error Margin

What are the assumptions of linear regression?

Assumptions of Linear Regression

You are testing hundreds of hypotheses with many t-tests. What considerations should be made?

Hundreds of Hypotheses

What statistical test could you use to determine which of two parcel types is better to use, given how often they are damaged?

Different Parcel Effectiveness

When an interviewer asks a question along the lines of:

<ul>
<li>What would your current manager say about you? What constructive criticisms might he give?</li>
<li>What are your three biggest strengths and weaknesses you have identified in yourself?</li>
</ul>

How would you respond?

When asked about your strengths in an interview, what is an effective way to respond?

When asked about your strengths in an interview, what is an effective way to respond?

Your Strengths and Weaknesses I

Which of the following is an acceptable strategy when discussing weaknesses in an interview?

Which of the following is an acceptable strategy when discussing weaknesses in an interview?

Your Strengths and Weaknesses II

What do you tell an interviewer when they ask you what your strengths and weaknesses are?

Your Strengths and Weaknesses

Brainteasers

When an interviewer asks you a question along the lines of:

<ul>
<li>Why did you apply to our company?</li>
<li>What are you looking for in your next job?</li>
<li>What makes you a good fit for our company?</li>
</ul>

How should you respond?

When asked 'What are you looking for in your next job?' in an interview, how can you tie the company's employee benefits into your response?

When asked 'What are you looking for in your next job?' in an interview, how can you tie the company's employee benefits into your response?

Why Do You Want to Work With Us I

How can company values be used effectively in an interview when asked 'What makes you a good fit for our company?'

How can company values be used effectively in an interview when asked 'What makes you a good fit for our company?'

Why Do You Want to Work With Us II

When responding to the question 'Why did you apply to our company?' during an interview, what aspect should you highlight?

When responding to the question 'Why did you apply to our company?' during an interview, what aspect should you highlight?

Why Do You Want to Work With Us III

How would you answer when an Interviewer asks why you applied to their company?

Why Do You Want to Work With Us

Describe a data project you worked on. What were some of the challenges you faced?

Describing a data project and its challenges

Hurdles In Data Projects

Analytics

How would you explain what a p-value is to someone who is not technical?

What does a p-value in a statistical test represent?

What does a p-value in a statistical test represent?

P-value to a Layman I

In a statistical test, how does a low p-value (less than 0.05) influence our decision about the null hypothesis?

In a statistical test, how does a low p-value (less than 0.05) influence our decision about the null hypothesis?

P-value to a Layman II

What are the assumptions of linear regression?

Which assumption of the residuals of the standard linear regression model can not be overcome by increasing the sample size?

Regression assumptions

Let’s say that you’re training a classification model.

How would you combat overfitting when building tree-based models?

Let's say that you're training a classification model.   How would you combat overfitting when building tree-based models?

Overfit Avoidance

How would you handle the data preparation for building a machine learning model using imbalanced data?

Addressing imbalanced data in machine learning through carefully prepared techniques.

Data Preparation for Imbalanced Data

Let’s say we’re comparing two machine learning algorithms. In which case would you use a bagging algorithm versus a boosting algorithm? 

Give an example of the tradeoffs between the two.

In machine learning, when would you use a bagging algorithm over a boosting algorithm?

In machine learning, when would you use a bagging algorithm over a boosting algorithm?

Bagging vs. Boosting

Bagging vs Boosting

Given a string, write a function to determine if it is palindrome or not.

Note: A palindrome is a word/string that is read the same way forward as it is backward, e.g. <code>&#39;reviver&#39;</code>, <code>&#39;madam&#39;</code>, <code>&#39;deified&#39;</code> and <code>&#39;civic&#39;</code> are all palindromes, while <code>&#39;tree&#39;</code>, <code>&#39;music&#39;</code> and <code>&#39;person&#39;</code> are not palindromes.

Example:

Input:

<pre tabindex="0" class="chroma"><code>word1 = &#34;tree&#34;
word2 = &#34;radar&#34;
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def is_palindrome(word1) -&gt; False
def is_palindrome(word2) -&gt; True
</code></pre>

Given a string, write a function to determine if it is palindrome or not.

String Palindromes

Write a SQL query to select the 2nd highest salary in the engineering department.

Note: If more than one person shares the highest salary, the query should select the next highest salary.

Example:

Input:

<code>employees</code> table

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>first_name</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>last_name</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>salary</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>department_id</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>
<code>departments</code> table

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>name</code></td>
<td>VARCHAR</td>
</tr>
</tbody>
</table>
Output:

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>salary</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>

Imagine you are asked to build a machine learning model to decide new loan approvals for a
financial firm. You ask the data department in the company for a subset of data to get started
working on the problem. The data includes different features about applicants such as age,
occupation, zip code, height, number of children, favorite color, etc. You decide to build
multiple machine learning models to test out different ideas before settling on the best one.

How would you explain the bias-variance tradeoff with regards to
building and choosing a model to use?

Bias vs. Variance Tradeoff

Explain the difference between the XGBoost and random forest algorithms and give an example where you would use one over the other.

Why might a data scientist choose to use XGBoost instead of Random Forest for a particular machine learning task?

Why might a data scientist choose to use XGBoost instead of Random Forest for a particular machine learning task?

XGBoost vs Random Forest: Choice

Xgboost vs Random Forest

How does random forest generate the forest? Additionally, why would we use it over other algorithms such as logistic regression?

What happens when you average the output on multiple decision trees?

What happens when you average the output on multiple decision trees?

Average Trees

Random Forest Explanation

What is the difference between Logistic and Linear Regression?

When would use one instead of the other in practice?

What is the difference between Logistic and Linear Regression?

Linear vs Logistic Regression

Let’s say that you work at a B2B SAAS company that’s interested in testing the pricing of different levels of subscriptions.

Your project manager comes to you and asks you to run a two-week-long A/B test to test an increase in pricing.

How would you approach designing this test? How would you determine whether the increase in pricing is a good business decision?

Testing Price Increase

A/B Testing

Tell me about a project in which you had to clean and organize a large dataset.

Describing a real-world data cleaning and organization project

Data Cleaning Experiences

Data Pipelines

Create a class named <code>LRUCache</code> that implements the behavior expected of a Least Recently Used (LRU) Cache.

The <code>LRUCache</code> class implements the following methods:

<ul>
<li><code>__init__(self, capacity)</code> - initializes the object and takes in a capacity parameter specifying the maximum capacity of the cache.</li>
<li><code>get_if_exists(self, key) -&gt; Any|None</code> - gets a value based on a key. If the key exists, returns the associated value. If the key has expired or did not exist in the first place, returns <code>None</code>.</li>
<li><code>put(self, key, value)</code> - places a value inside the cache. In the case wherein the cache is full, invalidates the least recently used element. When two keys collide, the older value should be invalidated in place of the new one.</li>
</ul>

Example:

<pre tabindex="0" class="chroma"><code>cache = LRUCache(2)
cache.put(&#34;sample&#34;, 55)
cache.get_if_exists(&#34;sample&#34;) # returns 55
cache.get_if_exists(&#34;key&#34;) # returns None
cache.put(&#34;hello&#34;, 10) 
cache.put(&#34;sample&#34;, 9)
cache.put(&#34;world&#34;, 5)
cache.get_if_exists(&#34;hello&#34;) # returns None
</code></pre>

Notes:

<ul>
<li>It is assured that the value in the <code>put</code> method will never receive a <code>None</code> value. Moreover, it is assured that the capacity will always be &gt; 0.</li>

<li>All operations must be O(1).</li>

<li>Aside from Python’s standard dictionary implementation (<code>{}</code>), you may not use any other built-in data structures.</li>
</ul>

LRU Cache 1

A team wants to A/B test multiple different changes through a sign-up funnel.

For example, on a page, a button is currently red and at the top of the page. They want to see if changing a button from red to blue and/or from the top of the page to the bottom of the page will increase click-through.

How would you set up this test?

Your team wants to run an AB test on the color (red or blue) and position (top/bottom of the page) of a button that links to a promotion.

How many different variants of the test should you run to see the effect that a red button has on the bottom of the page, if currently the button is blue and on the top of the page.

Your team wants to run an AB test on the color (red or blue) and position (top/bottom of the page) of a button that links to a promotion.

How many different variants of the test should you run to see the effect that a red button has on the bottom of the page, if currently the button is blue and on the top of the page.

A/B Test Variants

Button AB Test

Let’s say we want to launch a re-design of a landing page to improve the click-through rate. We can do this by implementing an AB test.
Given that we launch an AB test, how would you infer if the results of the click-through rate were statistically significant or not?

How can you accurately conclude if the results of an A/B test, conducted to evaluate the effectiveness of a landing page redesign, are statistically significant?

How can you accurately conclude if the results of an A/B test, conducted to evaluate the effectiveness of a landing page redesign, are statistically significant?

Statistically Significant Test

Precisely ascertain whether the outcomes of an A/B test, executed to assess the impact of a landing page redesign, exhibit statistical significance.

Given an array and a target integer, write a function <code>sum_pair_indices</code> that returns the indices of two integers in the array that add up to the target integer. If not found, just return an empty list.

Note: Can you do it on $O(n)$ time?

Note: Even though there could be many solutions, only one needs to be returned.

Example 1:

Input:

<pre tabindex="0" class="chroma"><code>array = [1 2 3 4] 
target = 5 
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def sum_pair_indices(array, target) -&gt; [0 3] or [1 2]
</code></pre>

Example 2:

Input:

<pre tabindex="0" class="chroma"><code>array = [3]
target = 6 
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>Do NOT return [0 0] as you can&#39;t use an index twice.
</code></pre>

Given an array and a target integer, write a function that returns the indices of two integers in the array that add up to the target integer.

Target Indices

Let’s say that your company is running a standard control and variant AB test on a feature to increase conversion rates on the landing page. The PM checks the results and finds a .04 p-value.

How would you assess the validity of the result?

Experiment Validity

Let’s say that you work at a bank that wants to build a model to detect fraud on the platform.

The bank wants to implement a text messaging service in addition that will text customers when the model detects a fraudulent transaction in order for the customer to approve or deny the transaction with a text response.

How would we build this model?

Let’s say that you work at a bank that wants to build a model to detect fraud on the platform.

The bank wants to implement a text messaging service that will text customers when the model detects a fraudulent transaction in order for the customer to approve or deny the transaction with a text response.

Which statement is true?

Let's say that you work at a bank that wants to build a model to detect fraud on the platform.

The bank wants to implement a text messaging service that will text customers when the model detects a fraudulent transaction in order for the customer to approve or deny the transaction with a text response.

Which statement is true?

Bank Fraud Model

Let’s say you work at Allstate. Allstate is running <code>N</code> online ads right now. The table <code>ads</code> contains all those ads, ranked by popularity via the <code>id</code> column (e.g., the entry with <code>id = 1</code> is the most popular, etc.).

Create a subquery or common table expression named <code>top_ads</code> containing the top 3 ads (by popularity) and return the number of rows that would result from the following operations

<ol>
<li><code>ads INNER JOIN top_ads</code></li>
<li><code>ads LEFT JOIN top_ads</code></li>
<li><code>ads RIGHT JOIN top_ads</code></li>
<li><code>ads CROSS JOIN top_ads</code></li>
</ol>

Note: Please make the <code>join_type</code> column in your output have the values <code>inner_join</code>, <code>left_join</code>, etc. for each of their respective join types

Note: Please return only one query with each number in a different row

Example:

Input:

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>id</code></td>
<td>INTEGER</td>
</tr>

<tr>
<td><code>name</code></td>
<td>VARCHAR</td>
</tr>
</tbody>
</table>
Output:

<table>
<thead>
<tr>
<th>Column</th>
<th>Type</th>
</tr>
</thead>

<tbody>
<tr>
<td><code>join_type</code></td>
<td>VARCHAR</td>
</tr>

<tr>
<td><code>number_of_rows</code></td>
<td>INTEGER</td>
</tr>
</tbody>
</table>

Size of Joins

Given an integer <code>N</code>, write a function that returns a list of all of the prime numbers up to <code>N</code>.

Note: Return an empty list there are no prime numbers less than or equal to <code>N</code>.

Example:

Input:

<pre tabindex="0" class="chroma"><code>N = 3
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>def prime_numbers(N) -&gt; [2,3]
</code></pre>

Given an integer N, write a function that returns all of the prime numbers up to N

Prime to N

Regularization and cross-validation are two common techniques used to improve the performance of machine learning algorithms.

When should you use one versus the other?

Given the choices below, which one is an example of a scenario where we should be using regularization?

Given the choices below, which one is an example of a scenario where we should be using regularization?

Regularization Example

Regularization and Validation

<h1>Binary Tree Validation</h1>

You are given the root of a binary tree. You need to determine if it is a valid binary search tree (BST).

A valid BST is defined as follows:

<ul>
<li>The left subtree of a node contains only nodes with values less than or equal to the node’s value.</li>
<li>The right subtree of a node contains only nodes with values greater than or equal to the node’s value.</li>
<li>Both the left and right subtrees must also be binary search trees.</li>
</ul>

Given the function <code>def is_valid_bst(root: Node) -&gt; bool:</code>, return True if the binary tree is a valid BST. Otherwise, return False.

<h3>Example:</h3>

Input:

<img src="https://d2qpirhrfplx04.cloudfront.net/9664817c-3e12-4a40-9236-8382b13f55a0.png" alt="image"/>Converted Binary Tree.png

Output:

<pre tabindex="0" class="chroma"><code>def is_valid_bst(Node(3)) -&gt; True
</code></pre>

Given the root node, verify if a binary search tree is valid or not.

Binary Tree Validation

What’s the difference between Lasso and Ridge Regression?

Under what circumstances is it recommended to use Lasso regression over Ridge regression?

Under what circumstances is it recommended to use Lasso regression over Ridge regression?

Lasso vs Ridge

How would you convey insights and the methods you use to a non-technical audience?

Making data-driven insights actionable for those without technical expertise

Simple Explanations

The Fibonacci sequence is a series of numbers in which each number is the sum of the two preceding ones, usually starting with 0 and 1. It is often used in algorithm examples, and is defined by the following formula: F(n) = F(n-1) + F(n-2), with F(0) = 0 and F(1) = 1.

Your task is to implement the Fibonacci algorithm in three different methods:
1. Recursively
2. Iteratively
3. Using Memoization

Example 1:

Input:

<pre tabindex="0" class="chroma"><code>n = 5
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>fibonacci(n) -&gt; 5
</code></pre>

Example 2:

Input:

<pre tabindex="0" class="chroma"><code>n = 10
</code></pre>

Output:

<pre tabindex="0" class="chroma"><code>fibonacci(n) -&gt; 55
</code></pre>

The Fibonacci sequence starts as follows: 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55…

This question requires the implementation of the Fibonacci sequence using three different methods: recursively, iteratively, and using memoization.

Implementing the Fibonacci Sequence in Three Different Methods

What’s the relationship between PCA and K-means clustering?

What does the variable “k” in k-means clustering refer to?

What does the variable "k" in k-means clustering refer to?

Input of K-means

PCA and K-Means

Challenge

Workiva Salaries by Position

Join our team at Workiva as an Engineering Manager of Artificial Intelligence and Machine Learning! As a pivotal member of our AI/ML team, your expertise will be instrumental in leading projects that demand innovative problem-solving, including the integration of cutting-edge Generative AI into our products. You will lead a high-impact team of machine learning and software engineers focused on building the intelligent backbone of the Workiva AI platform. You'll foster an environment of technical excellence and growth, mentor talent, address challenges and collaborate with Product and Data Science to deliver "aha!" moments for our products and customers. Are you passionate about pioneering the possibilities of Generative AI? Join us and be a part of the team driving innovation and shaping the future of AI/ML at Workiva! Discover more aboutWorkiva's Generative AI. <h5>What You'll Do </h5><h5>Leadership & Team Management </h5><ul> <li>Lead, mentor, and develop a team of machine learning and software engineers focused on building the intelligent backbone of the Workiva AI platform. </li> <li>Provide hands-on coaching, performance feedback, and growth opportunities for engineers at varying experience levels. </li> <li>Foster a collaborative, inclusive, and high-ownership team culture grounded in trust, accountability, and continuous improvement. </li> </ul><h5>Cross Functional Collaboration </h5><ul> <li>Collaborate closely with Product, Program Management, UX and UXR to lead and develop engineers in owning development, maintaining AI & ML infrastructure, and seamlessly integrating Generative AI and Machine Learning features into products </li> <li>Work with internal engineering teams and developers to understand integration needs and remove friction. </li> <li>Communicate complex technical issues to both technical and non-technical audiences effectively. </li> </ul><h5>Technical Strategy & Execution </h5><ul> <li>Oversee the design, implementation, and maintenance of foundational AI/ML services </li> <li>Guide architectural decisions to ensure platform scalability, reliability, and alignment with Workiva's long-term technical vision. </li> <li>Ensure engineering best practices around security, testing, operational excellence, and documentation </li> <li>Drive improvements in latency, service availability, developer experience, and integration usability across internal and external interfaces </li> </ul><h5>Operational Excellence </h5><ul> <li>Maintain high service availability and performance across various ML services </li> <li>Champion observability, incident response readiness, operational improvements and managing team's support rotations </li> <li>Reduce complexity through simplification, automation, and thoughtful system design </li> </ul><h5>What You'll Need </h5><h5>Minimum Qualifications </h5><ul> <li>Bachelor's degree in Computer Science, Engineering, Data Science or equivalent combination of education and experience </li> <li>7+ years of total experience in software engineering and/or Machine Learning, with at least 2 years of dedicated experience as an Engineering Manager </li> <li>Strong understanding of ML development cycles and toolsets </li> <li>Experience with core concepts of Generative AI such as RAG, Agentic frameworks, etc. </li> <li>Solid experience in delivering SaaS products, specifically hosted in AWS, Azure, or GCP </li> <li>Proven ability to manage senior individual contributors, resolve technical conflicts, and build a culture of psychological safety and high performance </li> </ul><h5>Preferred Qualifications </h5><ul> <li>Master's degree in Computer Science, Engineering, Data Science or equivalent combination of education and experience. </li> <li>Experience leading teams of up to 5 people, preferably with diverse skill sets and specializations </li> <li>Solid leadership skills in an Agile/Sprint working environment </li> <li>Proven experience working with product teams to integrate AI/ML features into a product </li> <li>Excellent problem-solving skills, with the ability to address customer needs and improve product experiences </li> <li>Background in cloud-native architectures (GCP, AWS, or similar). </li> <li>Familiarity with Kubernetes, microservices, and modern DevOps practices </li> </ul><h5>Working Conditions </h5><ul> <li>Willingness to travel up to 15% for team and corporate meetings, fostering relationships and representing company interests </li> <li>Reliable internet access for remote working opportunities </li> </ul>How You'll Be Rewarded Salary range in the US: $163,000.00 - $261,000.00 A discretionary bonus typically paid annually Restricted Stock Units granted at time of hire 401(k) match and comprehensive employee benefits package The salary range represents the low and high end of the salary range for this job in the US. Minimums and maximums may vary based on location. The actual salary offer will carefully consider a wide range of factors, including your skills, qualifications, experience and other relevant factors. Employment decisions are made without regard to age, race, creed, color, religion, sex, national origin, ancestry, disability status, veteran status, sexual orientation, gender identity or expression, genetic information, marital status, citizenship status or any other protected characteristic. Workiva is committed to working with and providing reasonable accommodations to applicants with disabilities. To request assistance with the application process, please email talentacquisition@workiva.com. Workiva employees are required to undergo comprehensive security and privacy training tailored to their roles, ensuring adherence to company policies and regulatory standards. Workiva supports employees in working where they work best - either from an office or remotely from any location within their country of employment. #LI-KP1

As a member of the Marketing Data & Insights team, the Lead Marketing Analyst, Program Insights serves as a strategic analytics partner to global Go-To-Market (GTM) teams. You will be responsible for quantifying the health, efficiency, and risk of specialized GTM programs focused on Financial Reporting, Governance, Risk, and Compliance (GRC), Sustainability, and industry-specific verticals. By translating complex data into actionable insights, you will help these solution areas navigate market shifts and optimize their specialized funnels to drive global growth. This role goes beyond reporting. The Lead Marketing Analyst applies strong analytical judgment, modern analytics tooling (including AI-enabled analysis), and business context to answer the "so what?". Identifying what's working, what's underperforming, where risk exists, how spend translates into outcomes, and how programs and measurement approaches should evolve to maximize pipeline impact and ROI. Why This Role Matters Workiva is scaling a complex, global, multi-motion GTM engine while investing deeply in data and AI. In this environment, understanding not just what happened, but what will happen next, is critical to durable, efficient growth for our GTM programs. The Lead Marketing Analyst, Program Insights is essential to making Workiva's GTM smarter, faster, and more confident in its decisions. This role moves the organization beyond retrospective reporting to forward-looking, insight-driven optimization. Identifying leading indicators of success and risk, diagnosing performance drivers, and recommending clear actions in real time. By shaping measurement best practices, improving data foundations, and elevating data literacy across GTM teams, this role directly influences program efficiency, pipeline performance, and executive confidence in marketing's impact, ultimately strengthening Workiva's long-term growth trajectory. What You'll Do Program Health, Optimization & Advanced Analytics <ul> <li>Serve as the primary analytics partner for assigned global GTM programs, holding end-to-end visibility into program health, performance, efficiency, and risk-surfacing risks early, recommending mid-flight optimizations, and directly shaping decisions on budget allocation, audience strategy, channel mix, and measurement. </li> <li>Use advanced analytics and AI-assisted techniques (e.g., trend detection, anomaly identification, forecasting, scenario analysis) to surface insights and risks early </li> <li>Diagnose drivers of over- or under-performance (channel mix, audience targeting, execution quality, coverage gaps, conversion friction) and recommend clear, prioritized actions </li> <li>Evaluate spend versus outcomes to assess efficiency and expected return, analyzing metrics such as cost per lead, pipeline contribution, stage conversion rates, and ROI to inform recommendations on where to scale, optimize, or reduce investment. </li> <li>Identify leading indicators of success or risk and recommend mid-flight optimizations </li></ul> Strategic Insights, Storytelling & Communication <ul> <li>Translate campaign, channel, and regional performance into clear, compelling insights for audiences ranging from marketing practitioners to senior leaders </li> <li>Articulate performance narratives clearly by defining what happened, why it matters, and which strategic changes are necessary </li> <li>Deliver structured, decision-oriented readouts that categorize actions into doubling down on success, optimizing or fixing underperformance, and deprioritizing low-impact activities </li> <li>Communicate complex data in a manner that is accessible, credible, and actionable for all stakeholders </li> </ul>Measurement Strategy & Best Practices <ul> <li>Recommend and drive improvements to how GTM performance is measured, not just reported-by proposing new metrics, refining definitions, and influencing how targets, benchmarks, and success criteria are set across programs, regions, and motions. </li> <li>Help evolve program-level metrics across pipeline creation, progression, efficiency, and impact </li> <li>Identify gaps, inconsistencies, or outdated approaches and propose best-practice solutions </li> <li>Support consistent measurement across regions, channels, and GTM motions </li></ul> Analytics & Reporting Enablement <ul> <li>Design and deliver decision-ready dashboards and analyses that enable self-service for GTM leaders, campaign owners, and regional marketers while providing clear strategic context and interpretation. </li> <li>Ensure data accuracy, consistency, and scalability across regions and GTM motions </li> <li>Partner with operations and data teams to improve data models and reporting foundations </li></ul> Cross-Functional Partnership <ul> <li>Work closely with GTM stakeholders (Campaigns, Demand, Digital, Regional Marketing) throughout planning, execution, and retrospectives </li> <li>Partner with Marketing Ops, Sales Ops, Finance, and Data teams to align investment, targets, and performance measurement </li> <li>Help raise overall data literacy and comfort with analytics across GTM teams </li></ul> .What You'll Need Minimum Qualifications <ul> <li>6+ years of experience within a data insights or analytics function, with an emphasis on marketing data and program performance insights </li> <li>Undergraduate degree or equivalent combination of relevant career experience and expertise </li> <li>Experience supporting B2B SaaS GTM teams </li> </ul>Preferred Qualifications <ul> <li>Proficient in delivering actionable insights and recommendations in a marketing analytics or program analytics role </li> <li>Demonstrated ability to translate technical data into executive-level "performance narratives" that influence budget allocation and strategic pivots </li> <li>Deep understanding of B2B marketing funnels, including pipeline attribution, lead-to-opportunity conversion, and ROI modeling </li> <li>Advanced proficiency in modern analytics tooling and SQL, with experience leveraging AI-assisted analysis to accelerate insight generation </li> <li>Experience operating within a large, matrixed organization, supporting global or multi-region marketing programs </li> <li>Strong stakeholder management skills with the ability to raise data literacy across non-technical marketing and sales teams </li> </ul>Working Conditions & Travel Requirements <ul> <li>Up to 10% travel for customer meetings and company events </li> <li>Reliable internet access for any period of time working remotely, not in a Workiva office </li> </ul>How You'll Be Rewarded Salary range in the US: $129,000.00 - $207,000.00 A discretionary bonus typically paid annually Restricted Stock Units granted at time of hire 401(k) match and comprehensive employee benefits package The salary range represents the low and high end of the salary range for this job in the US. Minimums and maximums may vary based on location. The actual salary offer will carefully consider a wide range of factors, including your skills, qualifications, experience and other relevant factors. Employment decisions are made without regard to age, race, creed, color, religion, sex, national origin, ancestry, disability status, veteran status, sexual orientation, gender identity or expression, genetic information, marital status, citizenship status or any other protected characteristic. Workiva is committed to working with and providing reasonable accommodations to applicants with disabilities. To request assistance with the application process, please email talentacquisition@workiva.com. Workiva employees are required to undergo comprehensive security and privacy training tailored to their roles, ensuring adherence to company policies and regulatory standards. Workiva supports employees in working where they work best - either from an office or remotely from any location within their country of employment. #LI-HS2 <h5 class="jobOverviewSubtitle">Required </h5> <h5 class="jobOverviewSubtitle">Preferred </h5> <h5 class="jobOverviewSubtitle">Job Industries </h5><ul> <li>Other </li> </ul>

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