Astellas is a global life sciences company committed to turning innovative science into VALUE for patients. We provide transformative therapies in disease areas that include oncology, ophthalmology, urology, immunology and women's health. Through our research and development programs, we are pioneering recent healthcare solutions for diseases with high unmet medical need. Learn more at Astellas.com (https://www.astellas.com/en)

Are you driven to make a real difference in the lives of patients?

We're seeking passionate individuals who thrive in dynamic environments, embrace new ideas, and aren't afraid to take intelligent risks. People who act with unwavering integrity and are deeply committed to making a tangible impact.

Purpose and Scope:

We are seeking an experienced Research Data Engineer to design, deliver, evolve, and operate domain-specific data solutions, integrations, and reusable, governed research data products across the R&D; technology landscape. The role applies fit-for-purpose technologies—including Databricks where appropriate—to meet Research & Discovery, translational science, analytics, reporting, AI, and scientific decision-making needs. Success is measured by trusted solutions with clear ownership, data contracts, quality expectations, metadata, service levels, and reusable consumption patterns.

Working with Research scientists and stakeholders, Data Product Owners, Domain Data Architects, Domain Data Modelers, Data Governance Specialists, Data Scientists, and Platform Engineers, this role translates prioritized scientific and business outcomes into production-ready data solutions and products. The role collaborates with Platform Engineers responsible for shared platform capabilities, standards, and enablement.

Responsibilities and Accountabilities:

- Design, build, test, deploy, maintain, and support reliable data pipelines, integrations, curated datasets, and reusable research data products from discovery through production.
- Partner with Data Product Owners, Research stakeholders, application teams,



and architects to translate roadmaps, requirements, and acceptance criteria into incremental engineering deliverables and production releases.
- Develop fit-for-purpose data solutions across the R&D; technology landscape using Databricks, KNIME and other appropriate database, integration, orchestration, and processing technologies.
- Integrate structured, semi-structured, and unstructured data from research, laboratory, scientific application, and external-provider systems to support scientific workflows, analytics, AI, and reusable data products.
- Transform scientific data into standardized and contextualized datasets while preserving provenance, experimental context, metadata, quality, and traceability.
- Translate approved data designs and scientific requirements into scalable technical data models and implementation patterns.
- Collaborate with Data Governance and Quality stakeholders to embed data ownership, quality, lineage, metadata, security, privacy, retention, validation, and other applicable compliance requirements using appropriate governance technologies, including Unity Catalog where applicable.
- Design and implement fit-for-purpose data interfaces and consumption patterns, including governed tables and views, APIs, semantic models, feature datasets, vector indexes, knowledge-graph assets, and agent-ready datasets, with appropriate versioning and backward compatibility.
- Support appropriate testing, data-quality controls, monitoring, and operational processes to help ensure reliable and trusted data solutions.
- Apply DevOps practices, including source-code management, CI/CD, automated testing, peer review, environment promotion, controlled releases, and reusable engineering standards, templates, components, and accelerators.
- Optimize SQL and Spark workloads, databases, integrations, compute, storage, and workflows for performance, reliability, scalability, resilience, and cost efficiency.
- Work in Agile delivery teams, contributing to backlog refinement, sprint planning, demonstrations, retrospectives, release planning, operational readiness, and measurable scientific and business outcomes.

📌 Research Data Engineer (India)
🏢 Astellas
📍 India