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."
