Principal Software Engineer- Data Platforms

ABOUT IDBS

The Principal Data Software Engineer plays a critical role in the hands-on delivery and evolution of IDBSs 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 nearterm delivery with longterm maintainability and ensures data platforms can sustainably support growing product data and regulatory demands.
In this role you will have the opportunity to:

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.

The essential requirements of the job include:
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 highquality 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 principallevel impact through technical delivery mentorship and collaboration across teams rather than through line management or pure architecture ownership.

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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Join our winning team today. Together well accelerate the real-life impact of tomorrows science and technology. We partner with customers across the globe to help them solve their most complex challenges architecting solutions that bring the power of science to life.

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