Data Engineer (Clinical)

Overview

Data engineer role on Zifo's Scientific Data Foundation team, building data pipelines, models and APIs that turn clinical trial content into queryable, linked data for pharma and biotech clients.

What you'll do

• 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, including chunking strategies, metadata enrichment, and vector store population.
• Build and maintain ETL/ELT workflows that transform unstructured clinical content into queryable, linked data across relational and graph stores.
• Implement data quality validation specific to clinical data, including protocol section classification accuracy, entity extraction completeness, and 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.
• Identify and analyze business needs to determine data-related requirements, and clean, transform, and organize big data for analysis.
• Conduct data analysis to identify trends, patterns, and insights, and develop data visualization tools to communicate them.
• Provide consulting input on guidelines, design ideas, standards, and best practices, and help stakeholders estimate, plan, and prioritize data engineering efforts.
• Build relationships with clients, internal teams, and partners' leadership teams.

What you'll need

• Expertise in Python, Spark, Java, or Scala for building data pipelines, warehouses, and data processing systems.
• Expertise in relational databases such as SQL and NoSQL.
• Strong understanding of NoSQL databases such as MongoDB, Cassandra, and HBase.
• Familiarity with data standards such as FAIR, JSON, OWL, and markup languages such as YAML.
• Understanding of AI/ML and data science concepts and cloud architecture.
• Bachelor's or Master's degree in data engineering, computer science, or a related field.
• Experience with scientific informatics solutions OR a background in biological science, chemistry, health sciences, or experience working in the life or health science industry.
• Experience with cloud computing platforms such as AWS, Azure, and Google Cloud Platform (GCP) for deploying and managing data infrastructure.
• Experience using version control systems such as Git for collaborative code development.
• Experience with data-focused solutions such as Snowflake, Databricks, Kafka, Cloudera, Knime, Airflow, etc.
• Experience with AI/ML and data science libraries such as Scikit-Learn, TensorFlow, PyTorch, and HuggingFace for preparing data for downstream AI/ML applications.
• Customer centricity to deliver at the highest standards and elevate customer experience.

Nice to have

• Familiarity with data visualization solutions such as Tableau, Power BI, and D3.js.
• Data engineering related certification from a cloud platform such as Azure, AWS, or Cloudera.
• Experience with clinical data.

Details

• Location: United States.