Associate Director, RDU IT Data Engineering - AstraZeneca

Location Bengaluru, Karnataka, India Job ID R-257181 Date posted 26/07/2026

Job Title: Associate Director, RDU IT Data Engineering

Global Career Level: E

Role: Individual contributor role

Location: Manyata Tech Park, Bangalore.

Local candidates who can join immediately are preferred.

About Alexion

At Alexion, our mission is to transform the lives of people affected by rare diseases through the development and delivery of innovative medicines as well as supportive technologies and healthcare services.

Introduction to role

As Associate Director, RDU IT Data Engineering, this role leads the design and delivery of next-generation, patient-centric data and AI platforms that power rare disease innovation. Acting as a hands-on technical leader, it builds highly scalable, reliable, and secure data products and pipelines on modern cloud platforms, embedding AI throughout the lifecycle to increase speed, quality, and resilience. The position supports responsible AI use in daily workflows. This includes AI copilots that speed up development and automated testing that improves quality. It also involves observability tools that detect issues early and governance that helps meet GDPR, HIPAA, and FAIR principles passionate about trustworthy data products. It raises the bar on engineering excellence while mentoring others in safe and effective AI use to help transform complex data into meaningful impact for patients.

Accountabilities

Solution delivery

Design and build cloud-native ELT/ETL data pipelines and domain-oriented data products on AWS and Snowflake that are scalable, cost-efficient, and resilient.

Define and implement patterns for batch, micro-batch, and event-driven integrations; optimize for performance, reliability, and security.

AI-accelerated development

Use AI copilots to scaffold SQL/Python/dbt code, generate unit/integration tests, suggest query optimizations, and infer schemas/mappings.

Employ AI to auto-generate technical docs, lineage summaries, and code comments; integrate prompt standards and review checkpoints into PR workflows.

Data quality, observability, and reliability

Implement data quality frameworks and SLAs/SLOs with AI-enabled anomaly and drift detection, and root-cause suggestions; create self-healing runbooks where feasible.

Instrument pipelines with metrics, logs, and traces; leverage AI to correlate incidents across orchestration, warehouse, and source systems.

Governance, privacy, and compliance

Operationalize data governance and privacy controls (RBAC/PBAC, encryption, retention) with AI-assisted PII detection, policy checks, and automated audit artifacts.

Ensure alignment with FAIR and TRUSTed data product principles; contribute to catalog metadata, semantic tags, and discoverability with AI-supported enrichment.

Performance, cost, and platform optimization