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. 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.

3. 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.

4. 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?"

5. Key Responsibilities

As a Data Analyst at Ameritas, your day-to-day work is dynamic and highly impactful. Your primary responsibility is to process and analyze complex datasets to support internal business stakeholders. This means you will frequently gather requirements from business leaders, extract the necessary data, and build automated reporting solutions that drive decision-making.

You will spend a significant portion of your time developing and maintaining reports and dashboards using Power BI and Cognos. This is not just about making charts look nice; it involves rigorous testing, ensuring data accuracy, and implementing metrics that track the performance of processes associated with Reinsurance Operations or enterprise Business Intelligence. When data issues arise, you will be the first line of defense, troubleshooting defects and investigating errors in the technology.

Collaboration is a massive part of this role. You will rarely work in a silo. Expect to partner closely with Data Engineering and IT admin teams to ensure data pipelines are reliable and reporting tasks are completed on schedule. Additionally, you will contribute to the documentation of processes and procedures, ensuring that our data governance remains strong and that your solutions are scalable for the future.

6. Role Requirements & Qualifications

We are looking for candidates who combine technical capability with a naturally curious demeanor and a drive for impact. Here is what makes a candidate highly competitive for this role:

• Must-have educational background – You must be actively enrolled in a college program (at least half-time) studying Computer Science, Data Analytics, Data Science, or a closely related field for the entire duration of the role.
• Must-have availability – You need the ability to commit to a 12-month internship, working full-time (30-40 hours/week) during the summer and part-time (15-20 hours/week) during the school year.
• Must-have technical skills – Extremely strong PC and Excel skills. You must possess keen analytical, problem-solving, and troubleshooting abilities.
• Nice-to-have technical skills – Experience in Python, SQL, and Power Query/Power Pivot is highly desired. Intermediate to advanced SQL is a major plus, as is coursework related to AI/Machine Learning, data management, or databases.
• Soft skills – Excellent verbal and written communication skills are non-negotiable. You must be a self-starter who can work independently while also building strong relationships within a team setting.

7. Common Interview Questions

The questions below represent the types of challenges you will face during your interviews. While you should not memorize answers, you should use these to recognize patterns in what we value: practical problem-solving, technical accuracy, and strong communication.

Technical and Data Manipulation

These questions test your hands-on ability to work with data using our core tools.

• How would you explain the difference between a LEFT JOIN and an INNER JOIN to a non-technical stakeholder?
• Walk me through the steps you take to clean a messy dataset in Excel using Power Query.
• Write a SQL query to find the top three highest-performing policies by revenue in a given dataset.
• Have you ever used Python to automate a repetitive data task? Tell me about it.

Data Visualization and Reporting

These focus on how you present data and build tools for business partners.

• Describe your process for building a new dashboard in Power BI from scratch.
• How do you handle a situation where a stakeholder asks for a metric that you believe is misleading or unhelpful?
• Tell me about a time you had to troubleshoot a broken report. How did you find the root cause?

Behavioral and Problem-Solving

These questions evaluate your cultural fit, curiosity, and ability to navigate ambiguity.

• Tell me about a time you had to learn a new tool or technology on the fly to complete a project.
• Describe a situation where you identified a flaw in an existing operational process. What did you do?
• How do you prioritize your work when multiple stakeholders are asking for data requests at the same time?
• Why are you interested in the insurance industry, and how do you align with Ameritas' mission of "Fulfilling Life"?

8. Frequently Asked Questions

Q: How technical are the interviews for this role? While you will not face software engineering-level algorithm questions, you must be highly proficient in data manipulation. Expect practical, scenario-based questions testing your knowledge of Excel, SQL, and data visualization principles.

Q: What is the working arrangement for this position? This is a hybrid role based out of either our Cincinnati, OH or Lincoln, NE offices. You will be expected to work partially in-office and partially from home, requiring you to be located within a commutable distance.

Q: How important is prior insurance industry knowledge? It is not required, but it is a strong differentiator. Demonstrating an understanding of basic insurance concepts (like premiums, claims, or reinsurance) shows proactive curiosity and helps you contextualize your analytical answers.

Q: What makes a candidate stand out in the final interview? The best candidates demonstrate a "self-starter" mentality. They don't just answer the technical questions; they ask insightful questions about how the data is currently being used and suggest ways they could drive immediate impact.

9. Other General Tips

• Master the STAR Method: When answering behavioral questions, strictly follow the Situation, Task, Action, Result framework. Be highly specific about the Action you took and quantify the Result whenever possible.
• Showcase Your Curiosity: We highly value a "naturally curious demeanor." Ask thoughtful questions at the end of your interviews about our tech stack, our data governance challenges, or how the team measures success.
• Bridge the Gap Between Tech and Business: Always frame your technical solutions in terms of business value. If you optimized a query, mention how much time it saved the operations team.
• Highlight Your Adaptability: You will be investigating defects and dealing with imperfect data. Share stories that highlight your patience, troubleshooting skills, and positive attitude when things don't go as planned.

10. Summary & Next Steps

Joining Ameritas Life Insurance as a Data Analyst is a chance to build a meaningful career while making a tangible impact on our customers' financial security. You will be stepping into a role that demands technical rigor, creative problem-solving, and a deep commitment to collaboration. By preparing thoroughly, you are setting yourself up to thrive in an environment that values professional development, inclusion, and a drive for excellence.

Focus your remaining preparation time on mastering your core tools—Excel, SQL, and Power BI—while refining your ability to tell compelling stories with data. Remember to reflect on your past experiences so you can clearly articulate how your analytical skills have solved real problems. We want to see the authentic, enthusiastic professional you are.

The compensation data provided reflects the standard intern salary band for this position. Because this role involves both full-time summer hours and part-time school year hours, your actual take-home pay will scale with your seasonal schedule. Use this information to ensure your financial expectations align with the program's structure before entering the final stages of the process.

You have the skills and the potential to excel in this process. Continue to explore interview insights, practice your technical delivery, and review additional resources on Dataford to refine your edge. Good luck—we look forward to seeing the impact you will bring to Ameritas!