Demandbase is a leading GTM™ company that empowers B2B brands by integrating Account Intelligence into every step of the buyer journey, enhancing customer interactions, and driving effective marketing and sales tactics. Headquartered in the San Francisco Bay Area, with offices in New York, Seattle, and international teams, Demandbase is recognized as a top workplace and is deeply committed to promoting diverse and inclusive careers.

For those interested in data analysis, Demandbase offers a dynamic role where creativity meets strategy. As a Data Analyst, you will collaborate with data engineers and research analysts to manage and analyze large datasets, and develop advanced algorithms to enhance data quality. Your role will involve using tools like SQL, R, and Python to generate actionable insights in a fast-paced environment. If you possess strong problem-solving skills, proficiency in data analytics, and a collaborative spirit, this position offers an excellent opportunity for growth and impact.

Explore the interview process and gain valuable insights with Interview Query to prepare yourself for a successful career at Demandbase.

Submitting Your Application

The first step is to submit a compelling application that reflects your technical skills and interest in joining Demandbase as a Data Analyst. Whether you were contacted by a Demandbase recruiter or have taken the initiative yourself, carefully review the job description and tailor your CV according to the prerequisites.

Tailoring your CV may include identifying specific keywords that the hiring manager might use to filter resumes and crafting a targeted cover letter. Furthermore, don’t forget to highlight relevant skills and mention your work experiences.

Recruiter/Hiring Manager Call Screening

If your CV happens to be among the shortlisted few, a recruiter from the Demandbase Talent Acquisition Team will make contact and verify key details like your experiences and skill level. Behavioral questions may also be a part of the screening process.

In some cases, the Demandbase data analyst hiring manager stays present during the screening round to answer your queries about the role and the company itself. They may also indulge in surface-level technical and behavioral discussions.

The whole recruiter call should take about 30 minutes.

Technical Virtual Interview

Successfully navigating the recruiter round will present you with an invitation for the technical screening round. Technical screening for the Demandbase data analyst role usually is conducted through virtual means, including video conference and screen sharing. Questions in this 1-hour long interview stage may revolve around Demandbase’s data systems, ETL pipelines, and SQL queries.

In the case of data analyst roles, take-home assignments regarding product metrics, analytics, and data visualization are incorporated. Apart from these, your proficiency against hypothesis testing, probability distributions, and machine learning fundamentals may also be assessed during the round.

Depending on the seniority of the position, case studies and similar real-scenario problems may also be assigned.

Onsite Interview Rounds

Followed by a second recruiter call outlining the next stage, you’ll be invited to attend the onsite interview loop. Multiple interview rounds, varying with the role, will be conducted during your day at the Demandbase office. Your technical prowess, including programming and ML modeling capabilities, will be evaluated against the finalized candidates throughout these interviews.

If you were assigned take-home exercises, a presentation round may also await you during the onsite interview for the data analyst role at Demandbase.

Quick Tips For Demandbase Data Analyst Interviews

• Understand Demandbase's Products: Demandbase's questions often rely heavily on situational scenarios with their products. Study Demandbase’s large array of products and understand how you would improve or test them.
• Be Data Driven: Demandbase’s data analytics interviews assess how well you can provide business-driving insights with data analysis. Brush up on your knowledge of statistics and probability, as these questions can be some of the hardest to solve.
• Embody the Spirit: Demandbase values collaboration, innovative thinking, and diversity. Practice responding to behavioral questions with answers that demonstrate your alignment with Demandbase’s core values.

Typically, interviews at Demandbase vary by role and team, but commonly Data Analyst interviews follow a fairly standardized process across these question topics.

• Write a function digit_accumulator to return the sum of every digit in a floating-point number string. You are given a string that represents some floating-point number. Write a function, digit_accumulator, that returns the sum of every digit in the string.

Example:

Input: python s = "123.0045" Output: ```python def digit_accumulator(s) -> 15

Since 1 + 2 + 3 + 0 + 0 + 4 + 5 = 15 ```

• How would you set up an A/B test for multiple changes in a sign-up funnel? A team wants to A/B test various changes in a sign-up funnel. For instance, on a page, a button is red and at the top. They want to see if changing the button’s color to blue and/or moving it to the bottom will increase click-through rates. How would you set up this test?

• How would you verify that an Instagram user is a high school student attending the school represented by their sticker? Instagram is releasing a new feature for high schoolers that allows users to identify their school and receive an associated sticker for their profile. How would you verify that a user is actually a high school student attending the school represented by their sticker?

• What is the probability that a red marble was pulled from Bucket #1? You have two buckets with different distributions of red and black marbles. Your friend pulls a red marble from one of the buckets. Calculate the probability that it was pulled from Bucket #1.

• What is the probability that two red marbles were pulled from Bucket #1? Your friend puts the red marble back and then draws two marbles sequentially, both of which are red. Calculate the probability that both red marbles were pulled from Bucket #1.

• What are time series models and why are they needed over simpler regression models? Explain what time series models are and discuss why they are necessary when simpler regression models are available.

• How would you determine if the difference between this month and the previous month is significant? You have a time series dataset grouped monthly for the past five years. Describe how you would find out if the difference between this month and the previous month is statistically significant.

• How would you analyze noisy and volatile asset price data to ensure accuracy? You are analyzing the price of a particular asset over time in a noisy and volatile dataset. Explain how you would analyze this data to ensure there are no discrepancies.

• How would you analyze noisy and volatile asset price data to ensure accuracy? You are working at a global trading company analyzing the price of a particular asset over time. The dataset is noisy, volatile, and not guaranteed to be completely accurate. How would you analyze this data to ensure there aren't any discrepancies?

If you're eager to join a team that values innovation, diversity, and career growth, Demandbase could be the right place for you! As a data analyst here, you'll have the unique opportunity to work with vast datasets, develop smart algorithms, and collaborate with a talented team, all while contributing to transforming how B2B businesses go to market. If you want more insights about the company, check out our main Demandbase Interview Guide, where we cover many interview questions that could be asked. At Interview Query, we empower you to unlock your interview prowess with a comprehensive toolkit, equipping you with the knowledge, confidence, and strategic guidance to conquer every challenge.

You can check out all our company interview guides for better preparation, and if you have any questions, don’t hesitate to reach out to us.

Good luck with your interview!