Senior Translational Data & AI Engineer

Job Title:

Senior Data & AI Engineer

Job Description

The Senior Data & AI Engineer plays a key role in driving scientific data engineering initiatives across the research pipeline, transforming complex multi‑modal scientific data into reliable, actionable assets. This is a hands‑on technical position focused on building modern, AI‑native data platforms, onboarding multiple clinical and biological studies, and modernizing data engineering practices in a cloud environment.

Responsibilities

• Drive scientific data engineering initiatives across the research pipeline by designing and implementing robust data platforms that support clinical, biological, and multi‑modal research data.

• Collaborate with IT, computational biology, and translational science leads to transform raw genomics, proteomics, and other assay data into curated, analysis‑ready datasets.

• Build and maintain orchestrated ingestion pipelines for external genomics, proteomics, and other assay data sources, including source input/output, table‑format writers, and row‑level reconciliation.

• Develop and harden layered transformation models (staging, intermediate, and data marts) with comprehensive real‑data test coverage, strong data‑quality guardrails, and reusable, consolidated transformation logic.

• Implement clinical data ingestion and reconciliation workflows aligned with recognized industry standards such as SDTM and ADaM, including subject and entity resolution.

• Deliver and maintain supporting platform infrastructure, including service APIs, CI/CD pipelines, containerised deployments, observability and monitoring instrumentation, and data‑warehouse performance tuning.

• Extract transformation logic and business rules from legacy analytical codebases (such as R or PySpark) and reconcile them with new platform implementations to ensure consistency and correctness.

• Translate scientific, biomarker, and bioinformatics requirements into durable data models and clearly defined, published data contracts that can be reused across teams.

• Identify repetitive or manual processes and convert them into automated workflows, guardrails, or reusable tooling, including AI‑assisted workflows that accelerate future development.

• Leverage AI coding agents and tooling to build systems and workflows around AI, rather than using them only for ad hoc prompting, and ensure appropriate guardrails around AI‑generated output.

• Participate in design and code reviews with an adversarial mindset, identifying edge cases, surfacing potential failure modes, and challenging suboptimal patterns to improve overall system quality.

• Contribute to onboarding multiple new scientific studies by focusing on either data ingestion or data visualization workflows, helping scale the platform to support 10+ additional studies.

• Collaborate closely with teammates to divide work effectively so that each engineer can independently own and deliver well‑defined components of the data platform.

• Continuously modernise data engineering and AI practices by adopting up‑to‑date tools, frameworks, and methodologies in cloud‑based data and AI environments.

Essential Skills

• Demonstrated AI‑native engineering practice, with hands‑on experience building systems and workflows around AI coding agents (such as Git Hub Copilot, Cursor, Codex, or equivalent), beyond simple prompting.

• Ability to recognise when repeated processes should be converted into automated pipelines and when AI agent output requires guardrails or additional infrastructure to ensure reliability.

• Bachelor's or master's degree in Computer Science, Data Engineering, Bioinformatics, or a closely related field.

• At least 4+ years of professional experience in data engineering with shipped production data pipelines on AWS, including services such as S3, ECS or Fargate, and Redshift or an equivalent massively parallel processing (MPP) data warehouse.

• Strong proficiency in Python for data engineering, including building data pipelines, transformations, and automation scripts.

• Strong proficiency in SQL, including writing complex queries and working with large‑scale analytical databases or data warehouses.

• Working knowledge of modern data engineering libraries and…