Senior Bioinformatics Data Engineer

Kaztronix invites a Senior Bioinformatics Data Engineer to drive the modernization of a biomarker data lake on AWS. The role focuses on building and operating production pipelines across oncology clinical studies, leveraging Dagster, dbt, Apache Iceberg on S3, and Redshift Gold to support downstream AI and visualization. The successful candidate will work closely with the lead engineer across the full data stack and will be based onsite in Wilmington, Delaware.

Responsibilities

• Design and maintain Dagster orchestrated ingestion pipelines for genomics vendors such as Caris, Predicine, Tempus, Olink, and CellCarta, including IO managers, Iceberg writing paths, and row-level accounting.
• Advance dbt workflows from Silver to Gold, emphasizing real-data test coverage, patterns for store failures, and the management of staging, intermediate, and mart models, plus macro consolidation.
• Develop clinical data ingestion paths aligned with SDTM and ADaM standards, implement reconciliation logic, and route subject dimensions effectively.
• Deliver platform infrastructure components such as FastAPI endpoints, CI/CD pipelines, containerized deployments, observability instrumentation, and Redshift performance tuning.
• Extract transformation rules from legacy R and PySpark code and harmonize them with 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 advocating for robust patterns.
• Collaborate with the lead engineer on design decisions and jointly sustain delivery velocity through paired working sessions and PR reviews.
• Maintain reproducibility standards across all work: CI on every PR, automated tests, and avoidance of ad hoc notebook-based production processes.

Requirements

• AI native engineering practice: proven experience building systems and workflows around AI coding agents (Claude Code, Cursor, Codex, or equivalent), not only prompting them. You recognize when a repetitive process should become an automated pipeline, implement guardrails for agent output, and construct infrastructure that accelerates future work.
• Education: Bachelor's or master's degree in computer science, data engineering, bioinformatics, or a related field.
• Experience: 5+ years of professional 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 such as Dagster, Airflow, or Prefect.
• Data quality instinct: track records 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 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 the ability to operate in an embedded pair model with tight feedback loops.

Technologies

• Dagster
• dbt
• Apache Iceberg
• Amazon S3
• Redshift
• FastAPI
• Docker
• Amazon ECS
• AWS Fargate
• Python
• SQL
• R
• PySpark
• Airflow
• Prefect
• CloudFormation
• AWS Glue Catalog
• CI/CD

Preferred Qualifications

• Direct experience with Apache Iceberg, AWS Glue Catalog, or lakehouse table formats.
• Comfort reading genomic data such as VAF, HGVS nomenclature, VCFs, CNV/fusion semantics, or rapid adaptability to new scientific domains.
• 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.