What is a Data Engineer at Aetna?

As a Data Engineer at Aetna, you are stepping into a role that sits at the intersection of healthcare innovation and massive data scale. Aetna, a CVS Health company, manages petabytes of data ranging from member claims and pharmacy records to clinical analytics and provider networks. Your work directly impacts how this data is ingested, processed, and served to downstream teams, ultimately driving decisions that improve patient outcomes and operational efficiency.

You will be responsible for designing and optimizing the critical data pipelines that power Aetna’s core analytical and digital products. Whether you are modernizing legacy infrastructure, building real-time streaming solutions for care management platforms, or ensuring strict data governance across cloud environments, your engineering decisions carry significant weight. The scale and complexity of healthcare data mean you will constantly tackle challenges related to data quality, privacy, and performance.

This role is highly collaborative and strategically vital. You will partner closely with data scientists, software engineers, and product managers to translate complex business requirements into robust technical solutions. Expect a work environment that values stability, rigorous engineering standards, and a mission-driven culture where your technical contributions help make healthcare more accessible and effective.

Common Interview Questions

Getting Ready for Your Interviews

Thorough preparation is the key to navigating Aetna's interview process with confidence. Your interviewers want to see not just your ability to write code, but how you design systems, solve complex data problems, and operate within a highly regulated domain.

Focus your preparation on the following key evaluation criteria:

• Technical Proficiency – You must demonstrate strong foundational skills in Python and SQL. Interviewers will look for your ability to write clean, efficient, and scalable code to manipulate large datasets and build reliable ETL/ELT pipelines.
• Data Architecture and Problem Solving – You will be evaluated on your ability to design data models and architecture that meet business needs. This includes understanding trade-offs in storage, compute, and processing frameworks.
• Domain Awareness and Governance – Working with healthcare data requires a defensive engineering mindset. Interviewers will assess your awareness of data privacy, security best practices, and handling edge cases in messy, real-world data.
• Collaboration and Culture Fit – Aetna highly values teamwork, clear communication, and a patient-first mentality. You need to show how you navigate ambiguity, work with cross-functional stakeholders, and align with the company's broader mission.

Interview Process Overview

The interview journey for a Data Engineer at Aetna is designed to thoroughly evaluate your coding fundamentals, system design capabilities, and behavioral alignment. The process typically begins with an initial recruiter phone screen to discuss your background, compensation expectations, and general fit. This is followed by a technical screen, which heavily emphasizes Python and SQL proficiency. You should be prepared to write code in a live, shared environment, focusing on data manipulation and basic algorithmic problem-solving.

If you advance to the final round, you will face a comprehensive loop consisting of multiple sessions. This stage dives deeper into your technical expertise, covering advanced Python coding, complex SQL queries, and data pipeline architecture. You will also participate in behavioral interviews where engineering managers and stakeholders will assess your soft skills, past project experiences, and cultural fit.

Aetna’s interviewing philosophy balances technical rigor with a strong emphasis on practical, real-world application. Interviewers are less interested in trick questions and more focused on how you approach realistic data challenges, communicate your thought process, and consider the broader implications of your technical choices.

This visual timeline outlines the typical progression from your initial application through the technical screens and the final onsite loop. Use this to structure your preparation timeline, ensuring you are sharp on your Python and SQL fundamentals early on, while reserving time to practice system design and behavioral narratives for the final stages. Note that specific rounds may vary slightly depending on your seniority level or the specific team you are interviewing for.

Deep Dive into Evaluation Areas

To succeed, you need to understand exactly what your interviewers are looking for in each technical and behavioral domain. Below is a detailed breakdown of the core evaluation areas.

Python Coding and Algorithms

Python is heavily utilized across Aetna’s data engineering teams. This area evaluates your ability to write efficient, bug-free code to solve logic and data manipulation problems. Strong performance here means writing clean code, handling edge cases, and explaining your time and space complexity.

Be ready to go over:

• Data Structures – Proficiency with lists, dictionaries, sets, and tuples, and knowing when to use each for optimal performance.
• Data Manipulation – Using standard libraries or frameworks like Pandas to filter, aggregate, and transform datasets.
• String and Array Manipulation – Common algorithmic challenges involving parsing logs, cleaning messy strings, or processing sequences of data.
• Advanced concepts (less common) – Object-oriented programming principles, generators, and writing custom decorators for pipeline logging.

Example questions or scenarios:

• "Write a Python function to parse a messy log file and extract specific error codes, returning a count of each."
• "Given a dataset of patient visit records, write a script to identify patients who have visited more than three times in a rolling 30-day window."
• "Implement a function to merge two large, overlapping datasets and resolve duplicate entries based on a specific timestamp."

SQL and Data Modeling

SQL is the backbone of data engineering at Aetna. You will be tested on your ability to extract insights from complex relational databases and design schemas that support efficient querying. Interviewers look for your ability to go beyond basic SELECT statements and utilize advanced SQL features.

Be ready to go over:

• Complex Joins and Aggregations – Understanding the nuances of inner, outer, left, and cross joins, and aggregating data accurately.
• Window Functions – Using ROW_NUMBER(), RANK(), LEAD(), and LAG() to perform complex analytical queries over partitions of data.
• Query Optimization – Identifying bottlenecks in slow queries, understanding execution plans, and using indexes effectively.
• Advanced concepts (less common) – Designing star and snowflake schemas, handling slowly changing dimensions (SCDs), and writing recursive CTEs.

Example questions or scenarios:

• "Write a SQL query to find the top 3 most prescribed medications per region, using window functions."
• "Given a claims table and a member table, design a query to calculate the average claim amount for members who have been active for at least one year."
• "How would you redesign this normalized relational schema into a dimensional model optimized for a daily reporting dashboard?"

Data Architecture and Pipelines

This area tests your high-level understanding of moving and storing data at scale. Aetna handles massive volumes of data, so you must demonstrate knowledge of modern data architectures. Strong candidates can discuss the trade-offs between different batch and streaming technologies.

Be ready to go over:

• ETL/ELT Concepts – Designing robust pipelines to extract data from source systems, transform it for analytics, and load it into a warehouse.
• Distributed Computing – High-level understanding of frameworks like Apache Spark or Hadoop, and how data is partitioned and processed across clusters.
• Cloud Data Platforms – Familiarity with cloud services (AWS, GCP, or Azure) and modern data warehouses (like Snowflake or Redshift).
• Advanced concepts (less common) – Real-time streaming architecture (Kafka), orchestration tools (Airflow), and data mesh principles.

Example questions or scenarios:

• "Walk me through how you would design a data pipeline to ingest daily batch files of claims data from external vendors."
• "If a critical ETL job fails halfway through, how do you ensure data integrity and design the pipeline to be idempotent?"
• "Compare the trade-offs between processing data in a nightly batch job versus a near real-time streaming approach for a fraud detection system."

Behavioral and Cultural Fit

Aetna places a high premium on collaboration, communication, and a patient-centric mindset. This evaluation area ensures you can work effectively within their corporate structure and align with their core values. Interviewers want to see empathy, resilience, and ownership.

Be ready to go over:

• Cross-functional Collaboration – How you work with non-technical stakeholders to gather requirements and set realistic expectations.
• Handling Ambiguity – Your approach to solving problems when requirements are unclear or constantly changing.
• Conflict Resolution – Navigating disagreements with team members or pushing back on unrealistic deadlines professionally.
• Advanced concepts (less common) – Leading large-scale technical migrations or mentoring junior engineers.

Example questions or scenarios:

• "Tell me about a time you had to explain a complex technical data issue to a non-technical stakeholder."
• "Describe a situation where a data pipeline you built failed in production. How did you handle the immediate fallout and prevent it from happening again?"
• "Give an example of a time you had to push back on a product manager's request because it compromised data security or system stability."