Job highlights

- Hybrid work arrangements (in office / work from home) from certain locations may be permitted in accordance with Responsible Flexibility Guidelines

- Bachelors or masters degree in Computer Science,Data Engineering,Health Informatics,Bioinformatics,Life Sciences,or a related technical discipline.

- Required Qualifications

- Preferred Qualifications

Job Summary

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 RD 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 RD 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.

Required Qualifications

- Bachelors or masters degree in Computer Science, Data Engineering, Health Informatics, Bioinformatics, Life Sciences, or a related technical discipline.

- Seven or more years of experience building and supporting enterprise-scale data pipelines, curated datasets, and reusable data products on cloud data platforms.

- Strong SQL and Python skills, with experience in ETL/ELT, orchestration, distributed processing, data warehousing,



dimensional/canonical modelling, schema design, and API or interface development.

- Hands-on experience designing and supporting data integrations using REST APIs, relational and non-relational database connectivity, flat-file transfers, SFTP, cloud storage and managed integration services, and structured or semi-structured formats such as JSON, XML, CSV, and Parquet.

- Experience with Databricks, Apache Spark, Delta Lake, or comparable technologies and with production-grade data product operations.

- Experience implementing data contracts, quality controls, metadata, lineage, documentation, automated testing, Git-based development, CI/CD, monitoring, and schema evolution.

- Strong troubleshooting, performance-tuning, communication, and cross-functional collaboration skills.

- Experience working in Agile, product-oriented delivery environments with backlog-driven incremental releases.

Preferred Qualifications

- Experience supporting pharmaceutical or biotechnology Research Discovery.

- Familiarity with ELNs, LIMS, registration systems, assay platforms, laboratory instrumentation, and scientific data providers.

- Experience with Unity Catalog, dbt, KNIME or comparable transformation tooling, MLflow, vector search, semantic models, knowledge graphs, or generative AI.

- Familiarity with FAIR data principles, scientific ontologies, controlled vocabularies, metadata management, and regulated scientific environments.

Working Environment

- Hybrid work arrangements (on-site/work from home) from certain locations may be permitted in accordance with Responsible Flexibility Guidelines. The role may require collaboration across global time zones and limited international travel.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Research Data Engineer (Bengaluru)
🏢 ADP
📍 Bengaluru