Principal Data Engineer
gsk
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Experience: 5+ Years
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Bengaluru
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Posted 1 hours ago
Job Description
- Build and maintain our data foundation: Design, build, and run scalable, production-grade data pipelines that collect, clean, transform, and deliver data. Make sound architectural choices and help set best practices for the team.
- Create data products for analytics and AI: Build reusable datasets, feature tables, and, where needed, retrieval infrastructure (such as vector databases) that support dashboards, models, and AI applications. Define clear data contracts so every consumer gets consistent, quality-checked inputs.
- Keep our data accurate and trustworthy: Implement data quality checks, validation rules, monitoring, and alerting. Track data lineage (where data comes from and where it's used) so we're always audit-ready.
- Turn business questions into data solutions: Work with business partners, data scientists, and analysts to gather requirements, agree on delivery timelines and service levels, and deliver self-service data that answers real business questions.
- Prepare data for modeling: Create, version, and maintain the training and evaluation datasets used by data scientists. Make sure results are reproducible and that data quality supports model performance.
- Automate with AI tools/Agent : Build or use LLM-powered tools and agents to automate data checks, monitoring, and reporting. Set sensible guardrails and decide when human review is needed.
- Fix problems quickly and permanently: Investigate pipeline and data incidents, find the root cause, and implement lasting fixes that improve reliability.
- Share your knowledge: Set standards for code, documentation, and governance. Conduct code reviews and mentor junior and mid-level engineers.
- Keep improving: Refine frameworks, tools, and automation to reduce manual effort, and stay current with emerging data and AI engineering practices.