Engineer
Eli Lilly and Company11 hours ago
Bengaluru, IndiaSenior
Responsibilities
- Conduct contextual inquiry, interviews, usability testing, surveys, behavioral analysis, journey mapping, and workflow research with clinical, scientific, safety, regulatory, and business users.
- Translate user and business evidence into prioritized requirements, data contracts, design principles, capability requirements, evaluation criteria, and roadmap input.
- Design, build, and own production-grade Bronze, Silver, and Gold Lakehouse pipelines and domain data products on Databricks and AWS.
- Develop reusable metadata-driven, self-healing, AI-augmented pipeline frameworks with automated testing, anomaly detection, remediation, schema drift detection, and lineage capture.
- Apply DataOps, CI/CD, infrastructure-as-code, observability, and data quality practices while owning reliability, performance, and cost.
- Evaluate, select, configure, and prototype enterprise platforms and third-party solutions using fit-gap analysis and build/buy/configure recommendations.
- Deliver SLA-backed data products, publish documentation in the enterprise catalog, and advance federated data mesh and data ownership patterns.
- Mentor engineers, lead architecture and design reviews, establish engineering and research quality standards, and package reusable patterns for other squads.
Requirements
- Bachelor’s degree in Computer Science, Data Engineering, or a related discipline.
- Substantial senior-level hands-on experience independently delivering production-grade data engineering pipelines and platforms.
- Direct exposure to clinical and/or non-clinical data users and their workflows.
- Strong Python and SQL skills plus working proficiency with PySpark and Spark.
- Experience with Databricks, AWS, data modeling, Lakehouse and medallion patterns, Databricks Workflows, and Apache Airflow.
- Experience with Git, Azure DevOps or GitHub Actions, automated testing, Terraform or Bicep, CI/CD, and DevSecOps practices.
- Experience with data observability and quality tools such as Great Expectations, Monte Carlo, or Acceldata.
- Awareness of healthcare and clinical data standards including CDISC, SDTM, ADaM, or OMOP.
- Hands-on mixed-methods research experience with journey maps, personas, opportunity frameworks, and expert enterprise or scientific user populations.
- Experience converting user and business needs into adopted technology direction and evaluating, selecting, and configuring enterprise platforms or third-party solutions.
- Current, regular use of LLM-based assistants, Copilot-style tools, or agentic workflows for engineering and synthesis work.
- Experience building self-healing, self-monitoring pipelines and reusable automation frameworks.
- Preferred experience includes data mesh, data product architecture, API-first data design, data marketplaces, catalogs, self-service analytics, GxP or 21 CFR Part 11, MLOps, feature stores, real-world evidence, or patient-generated data pipelines.
Benefits
- Lilly describes a supportive and respectful work environment with accommodation support and equal employment opportunity policies.
Categories
Data Engineering
About Eli Lilly and Company
Eli Lilly and Company researches, develops, manufactures, and markets prescription medicines and biologics for patients, sold through healthcare providers, pharmacies, and payers. Core therapeutic areas include diabetes, oncology, immunology, neuroscience, and obesity. Founded in 1876 and headquartered in Indianapolis, it is a public company (NYSE: LLY) with global operations and is building a new advanced manufacturing site for gene therapy and other modalities in Lebanon, Indiana.