Ncino, Inc. is a leading cloud banking platform that empowers financial institutions to enhance their operations and customer experiences through innovative technology solutions.

As a Data Engineer at Ncino, you will be responsible for designing, building, and maintaining scalable data pipelines that facilitate the efficient processing and analysis of large datasets. Your key responsibilities will include developing data architectures, ensuring data quality and integrity, and collaborating with cross-functional teams to identify and implement data-driven solutions. Proficiency in SQL and a strong understanding of algorithms are crucial, as you will utilize these skills to optimize data retrieval and processing mechanisms. Additionally, experience with Python and analytics tools will enhance your ability to extract valuable insights from complex datasets. A great fit for this role embodies a detail-oriented mindset, strong problem-solving skills, and a commitment to continuous learning, aligning with Ncino's values of innovation and excellence.

This guide will help you prepare effectively for your interview by providing insights into the skills and responsibilities that are vital for success in the Data Engineer role at Ncino, while also highlighting the company’s commitment to leveraging data for transformative solutions.

The interview process for a Data Engineer at Ncino, Inc. is structured to assess both technical expertise and cultural fit within the company. The process typically unfolds in several key stages:

1. Initial Screening

The initial screening is a brief phone interview, usually lasting around 30 minutes, conducted by a recruiter. This conversation focuses on your background, relevant experiences, and understanding of the Data Engineer role. The recruiter will also gauge your alignment with Ncino's values and culture, as well as your enthusiasm for the position.

2. Technical Assessment

Following the initial screening, candidates typically undergo a technical assessment, which may be conducted via a video call. This stage involves a deep dive into your technical skills, particularly in SQL and algorithms, which are crucial for the role. Expect to solve coding problems and discuss your approach to data modeling, ETL processes, and database management. You may also be asked to demonstrate your proficiency in Python and your understanding of data analytics.

3. Onsite Interviews

The onsite interview process generally consists of multiple rounds, often ranging from three to five interviews with various team members. These interviews will cover a mix of technical and behavioral questions. You will be evaluated on your problem-solving abilities, experience with data pipelines, and your capacity to work collaboratively within a team. Additionally, expect discussions around product metrics and how you can leverage data to drive business decisions.

4. Final Interview

In some cases, a final interview may be conducted with senior leadership or a hiring manager. This stage is designed to assess your long-term vision, alignment with the company's goals, and your potential contributions to the team. It’s also an opportunity for you to ask questions about the company’s direction and culture.

As you prepare for your interviews, it’s essential to familiarize yourself with the types of questions that may arise during the process.

Here are some tips to help you excel in your interview.

Understand Ncino's Mission and Values

Familiarize yourself with Ncino's mission to transform the financial services industry through innovative technology. Understanding the company's core values and how they align with your own will help you articulate why you are a good fit for the team. Be prepared to discuss how your work as a Data Engineer can contribute to their goals and enhance their product offerings.

Highlight Your SQL and Algorithm Skills

Given the emphasis on SQL and algorithms in this role, ensure you can demonstrate your expertise in these areas. Prepare to discuss your experience with complex SQL queries, data modeling, and optimization techniques. Additionally, brush up on algorithmic concepts, as you may be asked to solve problems that require logical thinking and efficient data processing.

Showcase Your Python Proficiency

While SQL is crucial, Python is also an important skill for a Data Engineer at Ncino. Be ready to discuss your experience with Python, particularly in data manipulation and automation. Highlight any projects where you utilized Python to streamline data workflows or enhance data quality.

Prepare for Behavioral Questions

Ncino values collaboration and innovation, so expect behavioral questions that assess your teamwork and problem-solving abilities. Use the STAR (Situation, Task, Action, Result) method to structure your responses, focusing on how you’ve worked with others to overcome challenges or implement new solutions.

Emphasize Your Analytical Mindset

As a Data Engineer, your ability to analyze data and derive insights is key. Be prepared to discuss how you approach data analysis, including any tools or methodologies you use. Highlight your experience with product metrics and how you’ve leveraged data to drive decision-making in previous roles.

Familiarize Yourself with Company Culture

Ncino has a strong emphasis on a collaborative and innovative work environment. Research the company culture and be ready to discuss how you can contribute to it. Share examples of how you’ve fostered collaboration in past projects or how you’ve embraced innovation in your work.

Ask Insightful Questions

Prepare thoughtful questions that demonstrate your interest in the role and the company. Inquire about the team’s current projects, the technologies they use, or how they measure success in their data initiatives. This not only shows your enthusiasm but also helps you gauge if Ncino is the right fit for you.

By following these tips and preparing thoroughly, you’ll position yourself as a strong candidate for the Data Engineer role at Ncino. Good luck!

Ncino, Inc. Data Engineer Interview Questions

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at Ncino, Inc. The interview will likely focus on your technical skills in SQL, algorithms, and Python, as well as your ability to analyze data and understand product metrics. Be prepared to demonstrate your problem-solving abilities and your understanding of data engineering principles.

Technical Skills

1. Can you explain the differences between inner join, left join, and right join in SQL?

Understanding SQL joins is crucial for data manipulation and retrieval.

How to Answer

Discuss the purpose of each join type and provide examples of when you would use them in a data engineering context.

Example

“An inner join returns only the rows that have matching values in both tables, while a left join returns all rows from the left table and matched rows from the right table, filling in with NULLs where there are no matches. A right join does the opposite. For instance, if I need to analyze customer data alongside their transaction history, I would use a left join to ensure I capture all customers, even those without transactions.”

2. Describe a complex SQL query you wrote and the problem it solved.

This question assesses your practical experience with SQL and your problem-solving skills.

How to Answer

Outline the problem, the approach you took, and the outcome of your query.

Example

“I once had to aggregate sales data from multiple regions to identify trends. I wrote a complex SQL query that utilized window functions to calculate running totals and averages over different time periods. This helped the sales team adjust their strategies based on real-time data insights.”

3. How do you optimize SQL queries for performance?

Performance optimization is key in data engineering to handle large datasets efficiently.

How to Answer

Discuss techniques such as indexing, query restructuring, and analyzing execution plans.

Example

“To optimize SQL queries, I often start by analyzing the execution plan to identify bottlenecks. I then implement indexing on frequently queried columns and restructure the query to minimize the number of joins. For instance, I once reduced query execution time by 50% by creating an index on a large sales table.”

Algorithms and Data Structures

4. Can you explain a sorting algorithm and its time complexity?

Understanding algorithms is fundamental for data manipulation and processing.

How to Answer

Choose a sorting algorithm, explain how it works, and discuss its efficiency.

Example

“I often use the quicksort algorithm, which is efficient for large datasets. It works by selecting a pivot and partitioning the array into elements less than and greater than the pivot. Its average time complexity is O(n log n), making it suitable for most applications.”

5. How would you handle a situation where you need to process a large dataset that doesn't fit into memory?

This question tests your knowledge of data processing techniques.

How to Answer

Discuss methods such as batch processing, streaming, or using distributed systems.

Example

“In such cases, I would use batch processing techniques, breaking the dataset into smaller chunks that can be processed sequentially. Alternatively, I might leverage distributed computing frameworks like Apache Spark to handle the data across multiple nodes, ensuring efficient processing without memory overload.”

Data Analysis and Metrics

6. What metrics would you consider important for evaluating the performance of a data pipeline?

This question assesses your understanding of data engineering metrics.

How to Answer

Discuss key performance indicators (KPIs) such as throughput, latency, and error rates.

Example

“I consider throughput, which measures the amount of data processed in a given time, and latency, which indicates the time taken to process a single record. Additionally, monitoring error rates is crucial to ensure data quality and reliability in the pipeline.”

7. Describe a time when you had to troubleshoot a data pipeline issue. What steps did you take?

This question evaluates your problem-solving skills in a real-world scenario.

How to Answer

Outline the issue, your troubleshooting process, and the resolution.

Example

“When I encountered a data pipeline failure due to a schema change in the source data, I first checked the logs to identify the error. I then traced the data flow to pinpoint where the failure occurred and updated the pipeline to accommodate the new schema. After testing, I implemented monitoring to catch similar issues in the future.”

• Write a SQL query to select the 2nd highest salary in the engineering department. Write a SQL query to select the 2nd highest salary in the engineering department. If more than one person shares the highest salary, the query should select the next highest salary.

• Write a function to merge two sorted lists into one sorted list. Given two sorted lists, write a function to merge them into one sorted list. Bonus: Determine the time complexity.

• Create a function missing_number to find the missing number in an array. You have an array of integers, nums of length n spanning 0 to n with one missing. Write a function missing_number that returns the missing number in the array. Complexity of (O(n)) required.

• Develop a function precision_recall to calculate precision and recall metrics. Given a 2-D matrix P of predicted values and actual values, write a function precision_recall to calculate precision and recall metrics. Return the ordered pair (precision, recall).

• Write a function to search for a target value in a rotated sorted array. Suppose an array sorted in ascending order is rotated at some pivot unknown to you beforehand. You are given a target value to search. If the value is in the array, return its index; otherwise, return -1. Bonus: Your algorithm's runtime complexity should be in the order of (O(\log n)).

• Would you think there was anything fishy about the results of an A/B test with 20 variants? Your manager ran an A/B test with 20 different variants and found one significant result. Would you suspect any issues with these results?

• How would you set up an A/B test to optimize button color and position for higher click-through rates? A team wants to A/B test changes in a sign-up funnel, such as changing a button from red to blue and/or moving it from the top to the bottom of the page. How would you design this test?

• What would you do if friend requests on Facebook are down 10%? A product manager at Facebook reports a 10% decrease in friend requests. What steps would you take to address this issue?

• Why would the number of job applicants decrease while job postings remain the same? You observe that the number of job postings per day has remained constant, but the number of applicants has been decreasing. What could be causing this trend?

• What are the drawbacks of the given student test score datasets, and how would you reformat them for better analysis? You have data on student test scores in two different layouts. What are the drawbacks of these formats, and what changes would you make to improve their usefulness for analysis? Additionally, describe common problems in "messy" datasets.

• Is this a fair coin based on 10 flips resulting in 8 tails and 2 heads? You flipped a coin 10 times, resulting in 8 tails and 2 heads. Determine if the coin is fair based on this outcome.

• How do you write a function to calculate sample variance for a list of integers? Write a function that outputs the sample variance given a list of integers. Round the result to 2 decimal places. Example input: test_list = [6, 7, 3, 9, 10, 15]. Example output: get_variance(test_list) -> 13.89.

• Is there anything suspicious about an A/B test with 20 variants where one is significant? Your manager ran an A/B test with 20 different variants and found one significant result. Would you consider this result suspicious? Explain your reasoning.

• How do you find the median of a list where more than 50% of the elements are the same in O(1) time and space? Given a sorted list of integers where more than 50% of the list is the same integer, write a function to return the median value in O(1) computational time and space. Example input: li = [1, 2, 2]. Example output: median(li) -> 2.

• What are the drawbacks of the given student test score datasets, and how would you reformat them for better analysis? You have data on student test scores in two different layouts. Identify the drawbacks of these layouts, suggest formatting changes for better analysis, and describe common problems in "messy" datasets. Refer to the provided image of the datasets.

• How would you evaluate whether using a decision tree algorithm is the correct model for predicting loan repayment? You are tasked with building a decision tree model to predict if a borrower will pay back a personal loan. How would you evaluate if a decision tree is the right choice, and how would you assess its performance before and after deployment?

• How does random forest generate the forest, and why use it over logistic regression? Explain the process by which random forest generates its ensemble of trees. Additionally, discuss why one might choose random forest over logistic regression for certain problems.

• When would you use a bagging algorithm versus a boosting algorithm? Compare two machine learning algorithms. Describe scenarios where you would prefer a bagging algorithm over a boosting algorithm, and discuss the tradeoffs between the two.

• How would you justify using a neural network for a business problem and explain its predictions to non-technical stakeholders? Your manager asks you to build a neural network model to solve a business problem. How would you justify the complexity of this model and explain its predictions to non-technical stakeholders?

• What metrics would you use to track the accuracy and validity of a spam classifier? You are tasked with building a spam classifier for emails and have completed a V1 of the model. What metrics would you use to evaluate the model's accuracy and validity?

If you want more insights about the company, check out our main ncino-inc Interview Guide, where we have covered many interview questions that could be asked. We’ve also created interview guides for other roles, such as software engineer and data analyst, where you can learn more about Ncino Inc.'s interview process for different positions.

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 Ncino Inc. Data Engineer interview question and 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!