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
Tune Snowflake warehouses, queries, and dbt models; apply AI-driven recommendations to balance cost, performance, and concurrency.
Contribute reusable components, and templates to “golden paths” that embed best practices and AI guardrails.
Collaboration and enablement
Partner with data science and analytics teams on data contracts, feature-ready datasets, and reproducible pipelines; support containerized/serverless runtimes where needed.
Mentor engineers on modern data engineering and responsible AI usage, including prompt engineering, validation patterns, and bias/quality checks.
Essential Skills/Experience
Master’s degree in Computer Science, Information Systems, Engineering, or a related field.
10+ years of experience in data engineering, data management, and analytics with a track record of delivering large-scale, secure, and resilient solutions—ideally in life sciences.
Strong hands-on expertise:
SQL and Python; building robust ETL/ELT and orchestration (Apache Airflow, AWS Glue).
Snowflake: resource monitors, RBAC, warehouse sizing, performance tuning, zero-copy clone, data sharing, time travel, Streams/Tasks, SnowPipe; tooling such as SnowSQL, Streamlit, and Cortex.
dbt and Fivetran; designing modular, testable transformations with version control and CI/CD.
AI in data engineering:
Practical experience using AI copilots for code/test generation with human review; AI-assisted schema mapping, documentation, and lineage.
AI-enabled data quality/observability (anomaly/drift detection, incident triage) and self-healing playbooks.
Automated PII detection/tagging and policy checks to support GDPR/HIPAA compliance.
Data governance and reliability:
Familiarity with FAIR and TRUSTed data product principles; experience with data catalogs and metadata standards.
Knowledge of data quality and observability methods and tools; ability to integrate telemetry across pipelines and platforms.
Cloud and platform skills:
AWS architecture patterns (certification preferred), Infrastructure as Code, GitHub-based CI/CD, secrets management.
Experience with containerization and serverless patterns; ability to support DS/ML adjacent workloads.
Experience implementing IaC with Terraform (or CloudFormation)
Communication and leadership:
Ability to explain complex technical concepts to varied audiences and to mentor engineers on best practices and responsible AI.
Desirable Skills/Experience
5+ years in biotech/pharma with exposure to R&D and/or commercial analytics use cases; understanding of compliance contexts (e.g., GxP exposure helpful).
Experience across multiple clouds or stacks (Azure, GCP, Databricks).
Familiarity with Kubernetes/Docker for data workloads and knowledge graph concepts.
When we put unexpected teams in the same room, we unleash bold thinking with the power to inspire life-changing medicines. In-person working gives us the platform we need to connect, work at pace and challenge perceptions. That's why we work, on average, a minimum of three days per week from the office. But that doesn't mean we're not flexible. We balance the expectation of being in the office while respecting individual flexibility. Join us in our unique and ambitious world.
AstraZeneca offers the chance to work at the forefront of rare disease biopharma where complex biology meets advanced technology to create transformative medicines for people with devastating conditions. At Alexion within AstraZeneca Rare Disease, work is driven by a deep connection to patients’ lived experiences and a clear purpose that shapes decisions every day. The environment combines the agility of a biotech with the reach and resources of a global organization—encouraging curiosity, transparent science, ethical decision-making, and continuous learning. Teams collaborate across disciplines to explore rare opportunities in areas of high unmet need while investing in personal growth through tailored development programs that build both technical mastery and empathy for patients’ journeys.
Ready to help build AI-powered data platforms that change what is possible for people living with rare diseases? Apply now!
We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, sex, gender, gender expression, sexual orientation, age, marital status, veteran status, or disability status.
Date Posted
13-Aug-2026Closing Date
23-Aug-2026Alexion is proud to be an Equal Employment Opportunity and Affirmative Action employer. We are committed to fostering a culture of belonging where every single person can belong because of their uniqueness. The Company will not make decisions about employment, training, compensation, promotion, and other terms and conditions of employment based on race, color, religion, creed or lack thereof, sex, sexual orientation, age, ancestry, national origin, ethnicity, citizenship status, marital status, pregnancy, (including childbirth, breastfeeding, or related medical conditions), parental status (including adoption or surrogacy), military status, protected veteran status, disability, medical condition, gender identity or expression, genetic information, mental illness or other characteristics protected by law. Alexion provides reasonable accommodations to meet the needs of candidates and employees. To begin an interactive dialogue with Alexion regarding an accommodation, please contact [email protected]. Alexion participates in E-Verify.
Skills Required
- Master's degree in Computer Science, Information Systems, Engineering, or related field
- 10+ years of experience in data engineering, data management, and analytics
- Expert SQL and Python
- Design and build ELT/ETL pipelines and orchestration (Apache Airflow, AWS Glue)
- Deep Snowflake expertise (resource monitors, RBAC, warehouse sizing, performance tuning, zero-copy clone, data sharing, time travel, Streams/Tasks, SnowPipe) and Snowflake tooling (SnowSQL, Streamlit, Cortex)
- dbt and Fivetran for modular, testable transformations with version control and CI/CD
- Practical experience using AI copilots for code/test generation, schema mapping, documentation, and lineage
- AI-enabled data quality and observability (anomaly/drift detection, incident triage) and self-healing playbooks
- Automated PII detection/tagging and policy checks to support GDPR and HIPAA compliance
- Familiarity with FAIR and TRUSTed data product principles; experience with data catalogs and metadata standards
- AWS architecture patterns and cloud platform experience (certification preferred)
- Infrastructure as Code and experience implementing IaC with Terraform or CloudFormation
- GitHub-based CI/CD and secrets management experience
- Experience with containerization and serverless patterns to support data science and ML workloads
- Ability to explain complex technical concepts and mentor engineers on best practices and responsible AI
- 5+ years experience in biotech/pharma with exposure to R&D or commercial analytics (GxP exposure helpful)
- Experience across additional clouds/stacks (Azure, GCP, Databricks)
- Familiarity with Kubernetes/Docker for data workloads and knowledge graph concepts
- Local candidate able to join immediately
AstraZeneca Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about AstraZeneca and has not been reviewed or approved by AstraZeneca.
-
Fair & Transparent Compensation — Pay is considered competitive across many roles when total rewards are factored in. Senior scientific and leadership bands are described with high ranges that reinforce competitiveness at upper levels.
-
Strong & Reliable Incentives — Bonuses, equity eligibility in many salaried roles, and solid sales on‑target earnings with upside are emphasized as meaningful parts of compensation. These elements boost overall value even where base pay is not the very highest.
-
Retirement Support — A 401(k) program with a strong company match and immediate vesting is repeatedly cited as a standout. Generous retirement support is viewed as enhancing the total package relative to peers.
AstraZeneca Insights
What We Do
We're transforming the future of healthcare by unlocking the power of what science can do for people, society and the planet.







