Senior Translational Data and AI Engineer

Inductive Quotient Analytics is seeking a Senior Translational Data and AI Engineer to modernize the ingestion and transformation of biomarker and clinical data within Computational Discovery. The role involves building and maintaining data pipelines, ensuring data quality, and collaborating with the lead engineer on design decisions to enhance the platform's capabilities.

Responsibilities:

• Build and maintain orchestrated ingestion pipelines for external genomics, proteomics, and other assay data sources, including source IO, table-format writers, and row-level reconciliation
• Develop and harden layered transformation models (staging, intermediate, and mart) with real-data test coverage, data-quality guardrails, and reusable, consolidated logic
• Implement clinical data ingestion and reconciliation paths against recognized standards (e.g., SDTM, ADaM), including subject and entity resolution
• Deliver supporting platform infrastructure: service APIs, CI/CD pipelines, containerized deployments, observability instrumentation, and data-warehouse performance tuning
• Extract transformation logic and business rules from legacy analytical code (e.g., R, PySpark) and reconcile them against new platform implementations
• Translate scientific and biomarker requirements from research and bioinformatics partners into durable data models and published data contracts
• Identify repetitive processes and convert them into automated workflows, guardrails, or reusable tooling — including AI-assisted workflows that make future work faster
• 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 support delivery velocity through paired working sessions and PR reviews
• Ensure all work meets reproducibility standards: CI on every PR, automated tests, and no ad-hoc notebook-based production processes

Requirements:

• 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 a related field
• Experience: 4+ 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
• Solid, hands-on experience 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
• Working understanding of lakehouse architecture patterns, ETL processes, and schema design for complex multi-modal datasets
• Comfort working with scientific, biomarker, or other complex domain data — or a demonstrated ability to ramp on unfamiliar scientific domains quickly
• Ability to handle PHI-adjacent clinical data under client'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
• Experience building or maintaining tooling around AI coding agents (custom commands, subagents, evals, or guardrails) rather than only consuming them
• Direct experience with Apache Iceberg, AWS Glue Catalog, or lakehouse table formats
• Preferred fluency reading genomic data (VAF, HGVS nomenclature, VCFs, CNV/fusion semantics)
• 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