Associate Director, RDU IT Data Engineering

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.

• 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.

• 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.