# Director – Enterprise Data Engineering at AstraZeneca

AstraZeneca is hiring a Director – Enterprise Data Engineering in Chennai, India. Level rates it Little AI ●○○○; you can [apply on AstraZeneca](https://jobsbylevel.com/go/6440d00d-ff11-4bf6-876d-6410d7039121?utm_medium=ai-agent).

AI Level 1, AI centrality 20 out of 100. India - Chennai.

## Details

- Company: [AstraZeneca](https://jobsbylevel.com/companies/astrazeneca)
- AI level: AI Level 1 (score 20 out of 100)
- Location: India - Chennai
- Posted: November 12, 2026
- Apply: https://jobsbylevel.com/go/6440d00d-ff11-4bf6-876d-6410d7039121?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=director-enterprise-data-engineering-at-astrazeneca-f10b9c

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## Description

Job Title: Director – Enterprise Data Engineering GCL: F Introduction to role: Are you ready to define the enterprise standard for data engineering and turn strategy into measurable outcomes that accelerate insights to patients? In this role, you will establish the main direction for data engineering across AstraZeneca. You will be responsible for building and scaling a distributed service that provides trusted, high-quality, secure, and cost-efficient data products at enterprise scale. You will own the end-to-end build of our data engineering capability. This includes driving visibility and lineage, automated quality controls, FinOps governance, performance SLAs/SLOs, and regulatory compliance across crucial information domains. Working as part of a varied community of specialists, you will connect strategy to execution, partnering across business technology groups to standardize how data is acquired, transformed, orchestrated, and delivered for impact. Can you turn a long-term architecture into reusable accelerators that dozens of teams embrace and love? Accountabilities: Define and execute the enterprise data engineering strategy aligned to our 2030 Data Strategy; translate vision into a capability model, adoption roadmap, service tiers, and maturity milestones that build measurable business value. Build and lead a focused, high-performing team of specialists; set direction on technology perfection across acquisition, storage, ingestion, transformation, orchestration, CI/CD, and containerization using Snowflake, Fivetran, Dbt, DataOps.live, and SnowPark Container Services. Govern the standardization of end-to-end data engineering solutions aligned with enterprise architecture; establish enterprise practice as a foundational pillar of data management. Champion automation across the lifecycle—impact analysis, design, build, test, deploy—leveraging AI code generation (for example, Snowflake Cortex Code and GitHub Copilot) to increase velocity and quality while reducing defects and time-to-detect/time-to-resolve. Embed governance-as-code and preventative controls; design pipelines and patterns that achieve close to zero cost leakage on cloud infrastructure, and uplift performance metrics including latency, reliability, and quality through clear SLAs/SLOs. Operate and scale a federated service across business technology groups; enable alignment, capability uplift, and reuse via onboarding kits, templates, and self-service accelerators. Partner with leaders across data, analytics, AI, cloud infrastructure, and enterprise/domain/solution architecture; liaise with procurement, finance, legal, quality, cybersecurity, privacy, and vendor partners to ensure compliant, secure, and value-driven delivery. Drive adoption through education, enablement, and community practices; measure success through standardization, automation readiness, turnaround time to business value, capability maturity, and adoption velocity. Proactively raise, handle, and mitigate risks; ensure alignment to regulatory requirements (including privacy, GxP, SOx, and HIPAA as applicable) without slowing delivery. Essential Skills/Experience: Preferably 15+ years in data engineering leadership roles at an enterprise capacity. Strong hands-on experience in data engineering capability involving data acquisition, data storage, data ingestion, data transformation, data orchestration, CI/CD, containerization. Extensive practical experience engaging with Open table standards (for ex, Iceberg) and Open technical catalogs (for ex, Snowflake Horizon). Extensive practical experience handling Structured, Semi-Structured and Unstructured data assets and engineering. Mandatory (Must Have) Skills – Snowflake, Fivetran DBT, DataOps.live, SnowPark Container technology, Apache Iceberg, Snowflake Horizon, Snowflake Cortex software (CoCo), AWS S3. Optional (Nice to Have) Skills – Databricks, Snaplogic, Apache Polaris, GitHub Copilot, Claude Code, AWS Kiro, AWS Redshift, Docker,

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