Senior Bioinformatics Data Engineer (Consultant)

Key Responsibilities

• Build and maintain Dagster-orchestrated ingestion pipelines for genomics vendors (Caris, Predicine, Tempus, Olink, CellCarta), including IO managers, Iceberg writers, and row-level accounting.
• Develop and harden dbt Silver-to-Gold transformations: real-data test coverage, store-failures patterns, staging/intermediate/mart models, and macro consolidation.
• Implement clinical data ingestion paths (SDTM and ADaM), reconciliation logic, and subject-dimension routing.
• Deliver platform infrastructure: FastAPI endpoints, CI/CD pipelines, containerized deployments, observability instrumentation, and Redshift performance tuning.
• Extract transformation rules from legacy R and PySpark code and reconcile against new platform implementations.
• Identify repetitive processes and convert them into automated workflows, guardrails, or reusable tooling.
• Participate in adversarial design and code reviews, identifying edge cases and pushing back on suboptimal patterns.
• Collaborate with the lead engineer on design decisions and jointly own delivery velocity through paired working sessions and PR reviews.
• Ensure all work meets reproducibility standards: CI on every PR, automated tests, no ad-hoc notebook-based production processes.

Minimum Qualifications

• AI-native engineering practice: demonstrated experience building systems and workflows around AI coding agents (Claude Code, Cursor, Codex, or equivalent) - not just prompting them.
• You recognize when a repeated process should become an automated pipeline, when agent output needs guardrails, and when to build infrastructure that makes future work faster.
• Surface-level tool usage is insufficient.
• Education: Bachelor's or master's degree in computer science, Data Engineering, Bioinformatics, or related field.
• Experience: 5+ years of professional experience in data engineering with shipped production pipelines on AWS (S3, ECS/Fargate, Redshift or equivalent MPP).
• Strong proficiency in Python and SQL with working knowledge of modern data engineering libraries.
• Advanced proficiency with dbt and a workflow orchestration tool (Dagster, Airflow, or Prefect).
• Data quality instinct: track record of catching silent failures, questioning data correctness assumptions, and noticing lossy joins or incomplete deliveries.
• Solid understanding of lakehouse architecture patterns, ETL processes, and schema design for complex multi-modal datasets.
• Ability to handle PHI-adjacent clinical data under Incyte's contractor policy (background check, compliance training, VPN access).
• Willingness to work within legacy codebases (R, PySpark) to extract business rules and validate new implementations.
• Excellent communication skills and ability to work in an embedded pair model with tight feedback loops.

Preferred Qualifications

• Direct experience with Apache Iceberg, AWS Glue Catalog, or lakehouse table formats.
• Comfort reading genomic data (VAF, HGVS nomenclature, VCFs, CNV/fusion semantics) or demonstrated ability to ramp on unfamiliar scientific domains quickly.
• Familiarity with clinical data standards including SDTM, ADaM, and CDISC.
• Pharma, clinical research, or life sciences background.
• Experience with containerization (Docker/ECS) and infrastructure-as-code (CloudFormation).
• Proficiency in R for interoperability with bioinformatics teams.