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Tier-1 brand and Bengaluru metro increase density, but senior/director level reduces applicant pool.
Deep data engineering leadership and bioinformatics/platform collaboration requirements limit cross-industry transferability.
20+ years plus specific big-data, cloud, and CI/CD/tooling requirements create strict filtering criteria.
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Lead and mentor a team of data engineers building automated, scalable, and sustainable data pipelines to support evolving scientific needs in a complex data ecosystem.
Architect and drive the long-term strategy for industrialized, end-to-end data services including data ingestion, streaming, transformation, and AI/GenAI integration for GSK R&D.
Ensure best engineering practices, compliance with Quality Management System (QMS) frameworks, and collaborate across platforms and bioinformatics teams to deliver reliable, accessible data products.
20+ years of strong data engineering and software engineering experience, including high volume, high compute challenges.
Deep expertise in at least one programming language such as Python, Scala or Java, and experience with big data tools like Spark, Kafka, or Storm.
Cloud experience with platforms like AWS, Google Cloud, Azure, and familiarity with CI/CD pipelines, DevOps practices, Infrastructure as Code (e.g., Terraform).
Work Experience Required: 20+ years explicitly stated.
Senior technical leader combining hands-on engineering skills with strategic vision in large-scale, complex data environments supporting scientific R&D.
Experienced in defining and implementing scalable data engineering standards across multiple teams, including observability, performance optimization, and operational excellence.
Comfortable engaging with AI/ML and GenAI teams to integrate cutting-edge data practices (e.g., RAG, prompt engineering) and advancing organizational engineering culture and quality standards.