# Data Engineer - Pharma R&D at Roche

Roche is hiring a Data Engineer - Pharma R&D in Hyderabad, India. Level rates it Works on AI ●●●○; you can [apply on Roche](https://jobsbylevel.com/go/9c4f0e68-2a1b-4112-bab3-210b1febb9ec?utm_medium=ai-agent).

AI Level 3, AI centrality 70 out of 100. Hyderabad.

## Details

- Company: [Roche](https://jobsbylevel.com/companies/roche)
- AI level: AI Level 3 (score 70 out of 100)
- Location: Hyderabad
- Posted: October 27, 2026
- Apply: https://jobsbylevel.com/go/9c4f0e68-2a1b-4112-bab3-210b1febb9ec?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=data-engineer-pharma-r-d-at-roche-a0b6f4

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## Description

At Roche you can show up as yourself, embraced for the unique qualities you bring. Our culture encourages personal expression, open dialogue, and genuine connections, where you are valued, accepted and respected for who you are, allowing you to thrive both personally and professionally. This is how we aim to prevent, stop and cure diseases and ensure everyone has access to healthcare today and for generations to come. Join Roche, where every voice matters. The Position Job Description: The Data Engineer – Clinical Study Design sits at the intersection of data architecture, clinical science, and technology delivery, helping build the data foundation behind Study Designer, a digital product transforming how studies are designed. This role combines strong data engineering skills, an understanding of clinical study data and workflows, and technical curiosity to translate complex clinical data structures into reliable, scalable pipelines and models that power the platform's insights. The ideal candidate is curious, collaborative, AI-minded, and passionate about using data to enable smarter, faster, and more effective clinical study design. Key Responsibilities Build ingestion pipelines for clinical trial protocols, ICF documents, SmPCs, CSRs, and published articles (PubMed, CTIS, ClinicalTrials.gov) - handling PDF parsing, text extraction, and structured data normalization Design and implement data models in Amazon Aurora (relational) and GraphDB (knowledge graph) to represent trial design entities: endpoints, eligibility criteria, study arms, interventions, therapeutic areas, and their relationships Develop embedding and vectorization pipelines to prepare extracted clinical text for RAG-based retrieval in LangGraph agentic workflows - chunking strategies, metadata enrichment, and vector store population Build and maintain ETL/ELT workflows that transform unstructured clinical content into queryable, linked data across both relational and graph stores Implement data quality validation specific to clinical data - protocol section classification accuracy, entity extraction completeness, cross-reference integrity (NCT IDs, EudraCT numbers, MeSH terms) Build data serving APIs (Python/FastAPI) that expose curated datasets to the Angular frontend and LangGraph agent layer Set up data lineage tracking and audit trails to support regulatory traceability of AI-generated trial design recommendations Preferred Qualifications: Education: Bachelor's degree in Computer Science, Data Engineering, or a related discipline. 5-8 years of experience building production grade data platforms and pipelines. Experience with biomedical knowledge graphs (e.g., linking drugs -> targets -> diseases -> trials) Prior work with PubMed/MEDLINE data, ClinicalTrials.gov API, or EMA/CTIS data Apache Spark or Databricks for batch processing of large document Required Skills Python — Primary language; experience with PDF/document parsing libraries (PyMuPDF, pdfplumber, unstructured.io, or similar) SQL — Advanced PostgreSQL-compatible SQL (Aurora); schema design, migrations, query optimization, indexing strategies for clinical data volumes Graph Databases — Hands-on with Neptune, Neo4j, or similar; SPARQL or Cypher query language; ontology/knowledge graph modeling for biomedical entities AWS — Aurora (PostgreSQL), S3, Lambda, Step Functions, SQS/SNS, IAM; infrastructure for data pipeline orchestration NLP / Document Processing — Text extraction from PDFs, section classification, named entity recognition for clinical/biomedical text; familiarity with embedding models and vector stores (OpenSearch, pgvector, or Pinecone) FastAPI — Building data serving endpoints; async patterns; integration with the application backend AI/ML Data Infrastructure — Preparing data for LangChain/LangGraph consumption; RAG pipeline design (chunking, retrieval, reranking); prompt-data integration patterns Pipeline Orchestration — Experience with workflow orchestration tools (Airflow, Prefect,

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