Posted:1 week ago| Platform: Foundit logo

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Job Type

Full Time

Job Description

  • Design, build, and support data ingestion, transformation, and delivery pipelines across structured and unstructured sources within the enterprise data engineering.
  • Manage and monitor day-to-day operations of the data engineering environment, ensuring high availability, performance, and data integrity.
  • Collaborate with data architects, data governance, platform engineering, and business teams to support data integration use cases across RD, Clinical, Regulatory, and Commercial functions.
  • Integrate data from laboratory systems, clinical platforms, regulatory systems, and third-party data sources into enterprise data repositories.
  • Implement and maintain metadata capture, data lineage, and data quality checks across pipelines to meet governance and compliance requirements.
  • Support real-time and batch data flows using technologies such as Databricks, Kafka, Delta Lake, or similar.
  • Work within GxP-aligned environments, ensuring compliance with data privacy, audit, and quality control standards.
  • Partner with data stewards and business analysts to support self-service data access, reporting, and analytics enablement.
  • Maintain operational documentation, runbooks, and process automation scripts for continuous improvement of data fabric operations.
  • Participate in incident resolution and root cause analysis, ensuring timely and effective remediation of data pipeline issues.
  • Create documentation, playbooks, and best practices for metadata ingestion, data lineage, and catalog usage.
  • Work in an Agile and Scaled Agile (SAFe) environment, collaborating with cross-functional teams, product owners, and Scrum Masters to deliver incremental value
  • Use JIRA, Confluence, and Agile DevOps tools to manage sprints, backlogs, and user stories.
  • Support continuous improvement, test automation, and DevOps practices in the data engineering lifecycle
  • Collaborate and communicate effectively with the product teams, with cross-functional teams to understand business requirements and translate them into technical solutions
  • Must have Skills:

    Build and maintain data pipelines to ingest and update metadata into enterprise data catalog platforms in biotech or life sciences or pharma.
  • Hands-on experience in data engineering technologies such as Databricks, PySpark, SparkSQL Apache Spark, AWS, Python, SQL, and Scaled Agile methodologies.
  • Proficiency in workflow orchestration, performance tuning on big data processing.
  • experience in data engineering, data operations, or related roles, with at least 2+ years in life sciences, biotech, or pharmaceutical environments.
  • Experience with cloud platforms (e. g. , AWS, Azure, or GCP) for data pipeline and storage solutions.
  • Understanding of data governance frameworks, metadata management, and data lineage tracking.
  • Strong problem-solving skills, attention to detail, and ability to manage multiple priorities in a dynamic environment.
  • Effective communication and collaboration skills to work across technical and business stakeholders.
  • Strong problem-solving and analytical skills
  • Excellent communication and teamwork skills
  • Experience with Scaled Agile Framework (SAFe), Agile delivery practices, and DevOps practices.

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