- EDC Systems – Experience integrating and extracting data from Electronic Data Capture platforms.
- Data Privacy & Compliance – Understanding of GCP (Good Clinical Practice), HIPAA, and 21 CFR Part 11 compliance in data engineering.
- Advanced concepts (less common) – Handling complex biomarker data, genomic data pipelines, and real-world evidence (RWE) integrations.
Example questions or scenarios:
- "Walk me through how you would map raw EDC data into an SDTM-compliant format."
- "How do you handle mid-study updates to a clinical database without disrupting downstream analytics?"
- "Describe a time you identified a critical data discrepancy in a clinical dataset. How did you resolve it with the clinical team?"
Data Pipeline Architecture (ETL/ELT)
You will be evaluated on your ability to design, build, and maintain robust data pipelines. Interviewers want to see that you can handle large volumes of disparate data, automate workflows, and ensure high reliability. A strong candidate will focus on scalability, error handling, and data lineage.
Be ready to go over:
- Pipeline Orchestration – Using tools to schedule and monitor complex workflows.
- Data Transformation – Designing efficient ETL/ELT processes using Python and modern data processing frameworks.
- Cloud Infrastructure – Familiarity with cloud-based data warehousing and storage solutions.
- Advanced concepts (less common) – Streaming data architectures for real-time patient monitoring, infrastructure as code (IaC).
Example questions or scenarios:
- "Design a data pipeline that ingests daily lab results from a third-party vendor, cleans the data, and loads it into a centralized warehouse."
- "How do you design your pipelines to handle schema drift or unexpected null values in incoming clinical data?"
- "Explain how you would optimize an ETL job that is currently taking too long to run and delaying critical clinical reports."
SQL & Python Proficiency
Your hands-on coding ability is the engine of your day-to-day work. Interviewers will test your ability to write clean, efficient, and well-documented code. Strong performance means writing optimized queries and scripts that can handle complex joins, window functions, and data transformations without bottlenecks.
Be ready to go over:
- Advanced SQL – Complex joins, window functions, CTEs (Common Table Expressions), and performance tuning.
- Python for Data Engineering – Using libraries like Pandas or PySpark for data manipulation and scripting.
- Code Quality – Writing modular, testable code and utilizing version control (Git).
- Advanced concepts (less common) – Writing custom Python libraries for internal clinical data validation.
Example questions or scenarios:
- "Write a SQL query to find the latest lab result for each patient in a study, partitioned by patient ID and ordered by visit date."
- "Given a messy, nested JSON file of patient demographics, write a Python script to flatten the data and remove duplicates."
- "How do you approach testing and validating your SQL scripts before deploying them to production?"
Cross-Functional Collaboration & Leadership
As a Senior engineer, your impact extends beyond your code. Interviewers will assess your ability to lead projects, mentor juniors, and communicate with non-technical stakeholders. Strong performance involves demonstrating empathy, clear communication, and a proactive approach to solving business problems.
Be ready to go over:
- Stakeholder Management – Translating clinical requirements into technical engineering tasks.
- Mentorship – Guiding junior engineers and establishing best practices within the Latam team.
- Navigating Ambiguity – Driving projects forward when requirements are unclear or constantly shifting.
- Advanced concepts (less common) – Leading cross-regional technical initiatives between Latam and US/EU teams.
Example questions or scenarios:
- "Tell me about a time you had to push back on a clinical data manager's request because it was technically unfeasible."
- "Describe a situation where you had to lead a project with highly ambiguous requirements. How did you define success?"
- "How do you ensure your engineering team stays aligned with the broader goals of the clinical operations team?"
