1. What is a Data Analyst at Ameritas Life Insurance?

As a Data Analyst at Ameritas Life Insurance, your work is directly tied to our core mission: Fulfilling Life. We believe that everyone should be happy, healthy, and financially secure, and we provide the trusted financial products—ranging from life and disability insurance to wealth management—that make this possible. In this role, you are the engine that transforms raw information into the insights that drive these critical business decisions.

You will step into a highly collaborative, hybrid environment where your analysis impacts real-world operations, particularly within our Reinsurance and Business Intelligence teams. Whether you are building automated dashboards, investigating data defects, or optimizing operational metrics, your contributions help measure and improve the performance of our entire organization.

This position is an incredible opportunity to operate at the intersection of business strategy and technical execution. You will not only write queries and build visualizations, but you will also partner with cross-functional stakeholders—including Data Engineering and IT—to ensure our data ecosystem is robust, accurate, and actionable. Expect a role that challenges your technical abilities while heavily relying on your natural curiosity and drive for impact.

2. Common Interview Questions

3. Getting Ready for Your Interviews

Preparation is about more than just reviewing syntax; it is about demonstrating how you apply technical tools to solve ambiguous business problems. We want to see how you think, how you collaborate, and how you align with our organizational values.

Focus your preparation on these key evaluation criteria:

• Technical Proficiency – We evaluate your hands-on ability to manipulate, analyze, and visualize data. You must demonstrate competence in Excel, SQL, and visualization tools like Power BI or Cognos.
• Analytical Problem-Solving – Interviewers will look at how you break down complex operational processes. You can show strength here by walking us through your methodology for identifying defects, structuring metrics, and proposing data-driven solutions.
• Curiosity and Initiative – As a self-directed professional, you are expected to take ownership of your tasks. We assess your willingness to learn new technologies and your proactive approach to investigating data anomalies.
• Culture and Collaboration – At Ameritas, team building and inclusion are top priorities. We evaluate your communication skills and your ability to translate complex data concepts into clear, actionable insights for non-technical business partners.

4. Interview Process Overview

The interview process for a Data Analyst at Ameritas is designed to be thorough, fair, and reflective of the actual work you will do. You will typically begin with a recruiter phone screen to assess your baseline qualifications, availability, and alignment with our hybrid work model and long-term commitment expectations.

Following the initial screen, you will progress to a technical evaluation. This may involve a live data manipulation exercise or a take-home assessment focused on Excel, SQL, or Power BI. The goal here is to see how you handle real-world datasets, clean messy information, and extract meaningful metrics. The final stages consist of behavioral and technical interviews with the hiring manager and cross-functional team members. In these rounds, the focus shifts to your problem-solving framework, your communication style, and your ability to partner with stakeholders.

Throughout the process, our philosophy is highly collaborative. We are not trying to trick you with obscure brainteasers; rather, we want to see how you approach practical, operational challenges within the insurance and business intelligence space.

This visual timeline outlines the typical stages of our interview process, from the initial recruiter screen to the final panel interviews. Use this to pace your preparation—focusing first on your high-level narrative and availability, and then diving deep into your technical stack and behavioral examples as you progress toward the final rounds.

5. Deep Dive into Evaluation Areas

To succeed, you need to prove your capability across several core competencies. Our interviewers use a mix of technical scenarios and behavioral questions to gauge your readiness for the role.

Data Manipulation and Querying

Your ability to extract and transform data is the foundation of this role. We need to know that you can efficiently pull data from relational databases and manipulate it to serve business needs. Strong performance means writing clean, optimized queries and demonstrating advanced proficiency in spreadsheet software.

Be ready to go over:

• SQL Fundamentals – Writing SELECT statements, utilizing JOINs, GROUP BY, and window functions to aggregate data.
• Advanced Excel – Utilizing VLOOKUP/XLOOKUP, Pivot Tables, and Power Query/Power Pivot to clean and analyze datasets.
• Python (Optional but advantageous) – Using libraries like Pandas to automate data processing or handle complex transformations.

Example questions or scenarios:

• "Walk me through a complex SQL query you wrote to solve a specific business problem. How did you optimize it?"
• "Here is a raw dataset with missing values and inconsistencies. How would you use Excel or Power Query to clean this data for analysis?"

Data Visualization and Reporting

Data is only as valuable as the insights it provides to stakeholders. You will be evaluated on your ability to design intuitive, accurate, and visually compelling reports using tools like Power BI or Cognos.

Be ready to go over:

• Dashboard Design – Structuring visualizations to highlight key performance indicators (KPIs) effectively.
• Stakeholder Requirements – Translating vague business requests into concrete reporting metrics.
• Data Storytelling – Explaining what the data means to audiences who may not have a technical background.

Example questions or scenarios:

• "Tell me about a time you designed a dashboard for a non-technical team. How did you decide which metrics to include?"
• "If a business partner claims a metric on your Power BI dashboard looks 'wrong,' how do you go about troubleshooting the issue?"

Business Acumen and Operations Analysis

Understanding the context of the data is critical, especially in specialized areas like Reinsurance Operations. We evaluate your ability to grasp business processes and identify areas for continuous improvement.

Be ready to go over:

• Process Improvement – Assessing operational workflows and using data to find bottlenecks.
• Metric Development – Defining what success looks like for a specific business unit.
• Defect Investigation – Tracing technology errors back to their root cause using data logs.

Example questions or scenarios:

• "Describe a time when your analysis directly led to an improvement in a business process."
• "How do you approach learning a new business domain, such as life insurance or reinsurance, to ensure your metrics are relevant?"