Principal Software Engineer- Data Platforms

Overview

At IDBS, one of Danaher’s 15+ operating companies, our work saves lives—and we’re all united by a shared commitment to innovate for tangible impact. IDBS helps BioPharma organizations unlock the potential of AI/ML to improve the lives of patients. As a trusted long‑term partner to 80% of the top 20 global BioPharma companies, IDBS delivers powerful cloud software and services specifically designed to meet the evolving needs of the BioPharma sector.

Responsibilities

• Lead the hands‑on delivery of reliable, scalable data pipelines and datasets that power AI discoverability, analytics, reporting, and workflow automation across scientific, clinical, and enterprise data.
• Build, evolve, and operate data ingestion and processing capabilities for structured, semi‑structured, and unstructured data, supporting the transition from early prototypes through early adoption and general release.
• Implement and maintain rich metadata and data quality practices that enable cross‑record querying, traceability, and AI‑ready data access across experiments, files, inventory, and workflows.
• Partner closely with architects, AI engineers, workflow engineers, and domain experts to ensure data is usable, performant, and trustworthy for downstream GenAI and analytics use cases.
• Act as a technical leader by setting a high bar for data engineering practices, mentoring engineers through code and delivery, and unblocking complex data challenges within the team.

Essential Requirements

• Significant hands‑on experience building and operating production data pipelines that support analytics, AI/ML, and enterprise application use cases.
• Strong practical experience working with unstructured and structured data, including ingestion, transformation, enrichment, indexing, and lifecycle management.
• Proven ability to deliver high‑quality, production‑grade data systems with a focus on data quality, reliability, scalability, observability, and operational support.
• Experience enabling data for downstream AI and reporting use cases, including cross‑entity queries, contextual linking, and performant data access patterns.
• Demonstrated principal‑level impact through technical delivery, mentorship, and collaboration across teams.

Desirable (but not essential)

• Experience supporting GenAI, NLP, or AI‑driven discovery and reporting use cases through well‑designed data pipelines and curated datasets.
• Familiarity with cloud‑based data platforms and tooling, including Databricks and AWS, in production enterprise environments.
• Experience working with data in regulated or quality‑sensitive domains (e.g. life sciences or GxP‑aligned environments), including auditability and traceability considerations.

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