Principal Software Engineer - Data Platforms

ABOUT IDBS

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 Bio Pharma 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 Bio Pharma companies, IDBS delivers powerful cloud software and services specifically designed to meet the evolving needs of the Bio Pharma sector.

IDBS, a Danaher company, leverages 35 years of scientific informatics expertise to help organizations design, execute and orchestrate processes, manage, contextualize and structure data and gain valuable insights throughout the product lifecycle, from R&D through manufacturing. Known for its signature IDBS E‑Work Book software, IDBS has extended its flexible, scalable solutions to the IDBS Polar and PIMS cloud platforms to help scientists make smarter decisions with assured confidence in both GxP and non‑GxP environments.

The Principal Data Software Engineer plays a critical role in the hands‑on delivery and evolution of IDBS’s data platforms. Operating at principal level, the role focuses on building, operating and continuously improving reliable, scalable and compliant data capabilities that support AI, analytics and enterprise workflows. Acting as a senior technical leader, the Principal Engineer influences data engineering decisions through deep practical expertise, balances near‑term delivery with long‑term maintainability, and ensures data platforms can sustainably support growing product, data and regulatory demands.

• 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.

• 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, rather than through line management or pure architecture ownership.

• 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.