EverCommerce [Nasdaq: EVCM] is a leading service commerce platform on a mission to digitally transform the service economy with end-to-end SaaS solutions. Their innovative digital and mobile applications simplify and empower the lives of over 685,000 customers across Home & Field Services, Health Services, and Wellness industries.

Joining EverCommerce as a Data Analyst means becoming part of a dynamic team focused on delivering meaningful business insights from complex datasets. Whether you’re working on payment data analysis, performance marketing analytics, or healthcare reporting, you’ll collaborate closely with data engineers and scientists to derive actionable insights that drive informed decision-making.

This Interview Query guide will take you through the interview process, provide commonly asked questions, and offer valuable tips to help you succeed. Let's dive in!

Submitting Your Application

The first step is to submit a compelling application that reflects your technical skills and interest in joining EverCommerce as a Data Analyst. Whether you were contacted by an EverCommerce 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 EverCommerce 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 EverCommerce 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 EverCommerce 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 EverCommerce’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 EverCommerce 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 EverCommerce.

Quick Tips For EverCommerce Data Analyst Interviews

• Should contain three tips for interviewing for this specific company based on interview experiences.

Example:

You should plan to brush up on any technical skills and try as many practice interview questions and mock interviews as possible. A few tips for acing your EverCommerce interview include:

• Know Your EverCommerce Products: EverCommerce questions are standardized and rely heavily on situational scenarios with their products. Study EverCommerce’s large breadth of products and understand how you would personally improve or test them.
• Be Data Driven: EverCommerce’s data science interviews assess how well you can provide business-driving insights with data science. Brush up on your knowledge of statistics and probability, given these questions can be some of the hardest to solve.
• Embody the Spirit: EverCommerce at its core has a collaborative, employee-focused culture that values innovation. Practice responding to behavioral questions with answers that touch on EverCommerce’s core values.

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

• 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 from a 2-D matrix. 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. Write a function to search for a target value in the rotated array and return its index, or -1 if not found. Bonus: Achieve (O(\log n)) runtime complexity.

• Would you suspect anything unusual about the A/B test results with 20 variants? Your manager ran an A/B test with 20 different variants and found one significant result. Would you consider this result suspicious?

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

• Why might job applications be decreasing while job postings remain constant? You observe that the number of job postings per day has remained stable, 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 issues in "messy" datasets.

• Is this a fair coin? You flip a coin 10 times, and it comes up tails 8 times and heads twice. Determine if the coin is fair based on this outcome.

• Write a function to calculate sample variance from a list of integers. Create 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] Output: get_variance(test_list) -> 13.89

• Is there anything suspicious about the A/B test results with 20 variants? Your manager ran an A/B test with 20 different variants and found one significant result. Evaluate if there is anything suspicious about these results.

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

• What are the drawbacks and formatting changes needed for messy datasets? You have data on student test scores in two different layouts. Identify the drawbacks of these layouts, suggest formatting changes to make the data more useful for analysis, and describe common problems seen in messy datasets. Example datasets:

• How would you evaluate the suitability and performance of a decision tree model for predicting loan repayment? You are tasked with building a decision tree model to predict if a borrower will repay a personal loan. How would you evaluate whether a decision tree is the correct model for this problem? If you proceed with the decision tree, how would you evaluate its performance before and after deployment?

• How does random forest generate the forest and why use it over logistic regression? Explain how a random forest generates its forest of trees. Additionally, discuss why you might choose random forest over other algorithms like logistic regression.

• When would you use a bagging algorithm versus a boosting algorithm? Compare two machine learning algorithms. In which scenarios would you use a bagging algorithm versus a boosting algorithm? Provide examples of the tradeoffs between the two.

• How would you justify using a neural network model 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 track the accuracy and validity of the model?

What is the average salary for a Data Analyst at Evercommerce?

Q: What is EverCommerce's mission?

EverCommerce aims to digitally transform the service economy with tailored, end-to-end SaaS solutions, empowering over 685,000 customers across Home & Field Services, Health Services, and Wellness industries.

Q: What are the primary responsibilities of a Payment Data Analyst at EverCommerce?

Payment Data Analysts at EverCommerce focus on extracting, analyzing, and interpreting payment-related data to deliver insightful business recommendations. You'll collaborate closely with data engineers and scientists to ensure data precision and availability, deep dive into transaction data, and identify potential growth and cost-saving opportunities.

Q: What skills and experience are required for the Data Analyst position?

Candidates should have a Bachelor's degree in a relevant field, 3+ years of data analytics experience, strong SQL skills, and proficiency in data visualization tools like Looker or Power BI. Prior experience in the payments, fintech, or finance industry is highly desirable, along with exceptional communication and problem-solving abilities.

Q: What benefits and perks does EverCommerce offer?

EverCommerce offers flexible working arrangements (remote, in-office, or hybrid), robust health and wellness benefits, continued professional development through Udemy, a 401k with matching, flexible time-off, an Employee Stock Purchase Program, and a Student Loan Repayment Program.

Q: How can I prepare for an interview at EverCommerce?

To prepare for an interview at EverCommerce, research the company, understand their mission and values, and familiarize yourself with their SaaS solutions. Practicing common interview questions on Interview Query, focusing on problem-solving and technical skills relevant to the role will give you a competitive edge.

If you are excited about joining a dynamic and innovative team at EverCommerce, a company leading the digital transformation of the service economy, then you're in the right place. For more in-depth insights into EverCommerce, don’t miss our comprehensive EverCommerce Interview Guide, filled with potential interview questions tailored for various roles. Whether you are prepping for a data analyst interview or exploring other positions, Interview Query provides a wealth of resources to aid your preparation.

At Interview Query, we equip you with indispensable tools and knowledge, building your confidence to ace any interview challenge that comes your way. Explore all our company interview guides for detailed preparation strategies, and if you have any questions, we’re here to help.

Good luck with your interview journey!