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

The questions below represent the types of challenges you will face during your Aetna interviews. While you should not memorize answers, use these to understand the core concepts and patterns interviewers focus on. Practice explaining your thought process clearly as you work through them.

Python Coding & Algorithms

This category tests your ability to write functional, efficient Python code to manipulate data or solve algorithmic puzzles.

• Write a Python script to read a CSV file, filter out rows with missing values, and calculate the average of a specific column.
• Given a list of dictionaries representing patient records, write a function to group the records by diagnosis code.
• How would you optimize a Python script that is currently running out of memory when processing a massive text file?
• Implement a function to find the longest consecutive sequence of days a member was admitted to the hospital, given a list of admission dates.
• Write a Python program to parse a JSON response from an API and extract nested fields into a flat dictionary.

SQL & Data Modeling

These questions evaluate your fluency in querying databases and your understanding of how to structure data for analytical workloads.

• Write a query using window functions to find the second highest claim amount for each patient.
• Explain the difference between a star schema and a snowflake schema. When would you use each?
• How would you write a query to identify duplicate records in a table without using a unique identifier?
• Given a table of user logins, write a query to find the number of users who logged in on consecutive days.
• Walk me through how you would optimize a slow-running SQL query that joins three large tables.

Data Architecture & Pipelines

Interviewers use these questions to gauge your systems-level thinking and your practical experience with data infrastructure.

• Design a data pipeline to ingest streaming data from a mobile app and make it available for hourly reporting. What tools would you use?
• How do you handle late-arriving data in a daily batch ETL process?
• Explain the concept of idempotency in data pipelines. Why is it important, and how do you achieve it?
• Describe a time you had to migrate data from an on-premise database to a cloud data warehouse. What were the challenges?
• How would you design a system to monitor the data quality of a pipeline and alert the team if anomalies are detected?

Behavioral & Leadership

These questions assess your soft skills, your ability to work in a team, and your alignment with Aetna’s culture.

• Tell me about a time you had to work with a difficult stakeholder who had unrealistic expectations about a data delivery timeline.
• Describe a project where you had to learn a new technology or framework quickly to meet a deadline.
• Give an example of a time you identified a significant flaw in an existing data process. How did you address it?
• Tell me about a time you failed. What did you learn from the experience, and how did you change your approach moving forward?
• How do you prioritize your tasks when you receive multiple urgent requests from different teams simultaneously?

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."

Key Responsibilities

As a Data Engineer at Aetna, your day-to-day work revolves around building and maintaining the infrastructure that makes healthcare data actionable. You will spend a significant portion of your time designing, developing, and deploying scalable ETL/ELT pipelines. This involves extracting data from diverse sources—such as legacy relational databases, external vendor APIs, and flat files—and transforming it into clean, structured formats suitable for downstream analytics and machine learning models.

Collaboration is a constant in this role. You will regularly interface with data scientists to understand their model requirements, ensuring they have access to the right features and historical datasets. You will also work alongside product managers and business analysts to translate new business initiatives into technical data requirements. This requires strong communication skills and the ability to bridge the gap between technical execution and business strategy.

Furthermore, you will be responsible for ensuring the reliability and performance of existing data systems. This includes monitoring pipeline health, troubleshooting failed jobs, and optimizing slow-running SQL queries or Spark jobs to reduce compute costs. Given the sensitive nature of healthcare data, you will also play a critical role in enforcing data governance, implementing strict security protocols, and ensuring all data pipelines comply with HIPAA and internal privacy standards.

Role Requirements & Qualifications

To be a competitive candidate for the Data Engineer role at Aetna, you need a solid foundation in software engineering and data architecture, coupled with an understanding of enterprise-scale systems. The ideal candidate blends strong coding skills with a deep appreciation for data quality and security.

• Must-have skills – Expert-level proficiency in Python and SQL is non-negotiable. You must have hands-on experience building and maintaining data pipelines using modern ETL/ELT methodologies. Familiarity with relational databases, data warehousing concepts, and basic cloud infrastructure (AWS, GCP, or Azure) is essential. Strong problem-solving skills and the ability to write clean, version-controlled code are required.
• Nice-to-have skills – Experience with distributed processing frameworks like Apache Spark or PySpark will make you stand out. Familiarity with orchestration tools like Apache Airflow, containerization (Docker/Kubernetes), and CI/CD practices is highly beneficial. Prior experience working in the healthcare industry or handling sensitive, regulated data (HIPAA) is a significant advantage.
• Experience level – Typically, candidates need 3+ years of relevant experience in data engineering, software engineering, or a closely related field. For senior roles, 5+ years of experience with a track record of leading complex architecture designs is expected. Interns or entry-level candidates should demonstrate strong academic foundations and hands-on project experience with Python and SQL.
• Soft skills – Clear communication is critical. You must be able to articulate technical trade-offs to both technical and non-technical audiences. A strong sense of ownership, adaptability to changing requirements, and a collaborative mindset are crucial for success in Aetna’s team-oriented environment.

Frequently Asked Questions

Q: How difficult is the Python coding round for the Data Engineer role? The Python coding round is generally practical rather than highly theoretical. Expect questions focused on data manipulation, string parsing, and basic data structures (like dictionaries and lists) rather than abstract competitive programming puzzles. Focus your preparation on writing clean, bug-free code quickly.

Q: Does Aetna require prior healthcare industry experience? While prior experience with healthcare data (and an understanding of HIPAA regulations) is a strong plus, it is not strictly required. Strong foundational data engineering skills are the priority. However, demonstrating an interest in the healthcare domain and an understanding of data privacy will give you a competitive edge.

Q: What is the work culture and work-life balance like at Aetna? Aetna is known for offering a stable, supportive work environment with a strong emphasis on work-life balance. The culture is highly collaborative and less high-pressure than some hyper-growth tech startups. You will have the opportunity to work on impactful projects while maintaining a healthy personal life.

Q: How long does the interview process typically take? The end-to-end process usually takes between three to five weeks, depending on the availability of the interviewers and the speed of the recruiting team. Aetna communicates relatively clearly between rounds, but it is always acceptable to follow up with your recruiter if a week passes without an update.

Q: Will I be tested on specific cloud platforms or big data tools? While the technical screens focus heavily on Python and SQL, the final architectural rounds will likely touch on cloud platforms and big data tools. If the job description mentions specific tools (like Snowflake, AWS, or Spark), be prepared to discuss your experience with them or demonstrate a strong conceptual understanding of how they operate.

Other General Tips

• Communicate Your Thought Process: In both coding and SQL rounds, do not code in silence. Explain your approach, discuss trade-offs, and state your assumptions before you start typing. Interviewers at Aetna value your problem-solving journey as much as the final solution.

• Master the STAR Method: For behavioral questions, structure your answers using the Situation, Task, Action, Result framework. Be specific about your individual contributions, especially when discussing team projects, and quantify your results whenever possible (e.g., "reduced pipeline runtime by 30%").

• Focus on Data Quality and Security: Given Aetna's position in the healthcare industry, demonstrating a defensive engineering mindset is crucial. Proactively mention how you would implement data validation, error logging, and security best practices in your technical answers.

• Ask Insightful Questions: At the end of your interviews, ask questions that show you are genuinely interested in the role and the company's challenges. Ask about their current data stack migration, how they handle data governance at scale, or the specific business problems your team will be solving.

Summary & Next Steps

Interviewing for a Data Engineer position at Aetna is a rigorous but rewarding process. By joining Aetna, you are stepping into a role where your technical expertise directly supports initiatives that improve healthcare delivery and patient outcomes. The scale of the data and the complexity of the domain offer continuous opportunities for professional growth and technical challenge.

Your most critical preparation steps are solidifying your foundational skills in Python and SQL, and practicing how to articulate your system design and architectural decisions clearly. Remember that Aetna values engineers who not only write great code but also understand the broader business context, prioritize data quality, and collaborate effectively across teams. Approach your interviews with a problem-solving mindset and a readiness to tackle messy, real-world data scenarios.

The compensation data above provides a general baseline for the Data Engineer role. When interpreting these figures, consider that your actual offer will depend heavily on your specific location, years of experience, and performance during the interview process. Aetna’s total compensation package typically emphasizes stability and comprehensive benefits alongside base salary.

You have the skills and the potential to excel in this process. Continue to practice your coding fundamentals, refine your behavioral narratives, and review the additional insights and resources available on Dataford to ensure you are fully prepared. Stay confident, communicate clearly, and good luck with your interviews!