Engineer

About this role

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Skills you'll use

What you'll do

• Experience-Led Discovery & Data Product Definition
• Plan and run mixed-methods research — contextual inquiry, interviews, usability testing, surveys, and behavioral analytics — with scientific, clinical, safety, regulatory, and business communities to determine which data products should exist and why.
• Map user journeys and data workflows across Clinical and Non-Clinical domains, surfacing friction, workarounds, and unmet needs, and analyse product telemetry alongside qualitative findings so behaviour and stated need are read together.
• Translate evidence into prioritised, decision-ready requirements, data contracts, and design principles, distinguishing genuine capability gaps from usability, adoption, and change-management gaps.
• Data Platform & Pipeline Engineering
• Independently design, build, and own end-to-end pipelines spanning Bronze / Silver / Gold Lakehouse layers on the Databricks + AWS ecosystem, owning reliability, performance, and cost for assigned data products.
• Lead the design & build of metadata-driven, reusable pipeline frameworks that reduce time-to-data, and set and enforce squad-level engineering standards and patterns.
• Apply DataOps practices — automated testing, CI/CD, infrastructure-as-code (Terraform / Bicep), observability — and lead architecture and design reviews, surfacing risks and trade-offs early.
• AI & Automation-Driven Engineering
• Build self-healing, AI-augmented pipelines using anomaly detection and automated remediation to reduce manual intervention and improve reliability.

Who can apply

• Plan and run mixed-methods research — contextual inquiry, interviews, usability testing, surveys, and behavioral analytics — with scientific, clinical, safety, regulatory, and business communities to determine which data products should exist and why.
• Map user journeys and data workflows across Clinical and Non-Clinical domains, surfacing friction, workarounds, and unmet needs, and analyse product telemetry alongside qualitative findings so behaviour and stated need are read together.
• Translate evidence into prioritised, decision-ready requirements, data contracts, and design principles, distinguishing genuine capability gaps from usability, adoption, and change-management gaps.
• Data Platform & Pipeline Engineering
• Independently design, build, and own end-to-end pipelines spanning Bronze / Silver / Gold Lakehouse layers on the Databricks + AWS ecosystem, owning reliability, performance, and cost for assigned data products.
• Lead the design & build of metadata-driven, reusable pipeline frameworks that reduce time-to-data, and set and enforce squad-level engineering standards and patterns.
• Apply DataOps practices — automated testing, CI/CD, infrastructure-as-code (Terraform / Bicep), observability — and lead architecture and design reviews, surfacing risks and trade-offs early.
• AI & Automation-Driven Engineering
• Build self-healing, AI-augmented pipelines using anomaly detection and automated remediation to reduce manual intervention and improve reliability.
• Apply LLM- and agent-based tooling to accelerate pipeline development, testing, and documentation, and to automate data quality checks, schema drift detection, and lineage capture.

About Eli Lilly

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