Senior Specialist, External and Scientific Engagement (ESE) Data Engineer

Join a global healthcare biopharma company as a Senior Specialist, External and Scientific Engagement (ESE) Data Engineer. The role involves designing, building, and operating reliable, scalable data pipelines and curated data layers to enable analytics and data-driven products. The Data Engineer will partner closely with upstream data owners, QA, business users, and DevOps to ensure data is delivered accurately, tested, and deployed. The position requires strong communication and team-working skills, with the ability to provide clear, proactive communication across time zones, provide constructive feedback, and foster collaboration and knowledge sharing among onshore and offshore teammates.

Requirements

• Design, implement, and operate scalable, reliable data pipelines and curated consumption layers to meet analytics and business needs using the team’s Databricks platform and PySpark/Spark Data Frame transformations.
• Develop clean, modular Python code and SQL-based models for data processing, validation, and consumption; build and maintain automated tests to ensure correctness and prevent regressions.
• Orchestrate, schedule, and monitor end-to-end workflows using Airflow; ensure pipelines are idempotent, resilient, and have clear retry and alerting behavior.
• Optimize performance and cost by applying best practices in Spark processing, SQL tuning, and Delta Lake/table management on Databricks.
• Use GitHub for source control, code reviews, and CI/CD integration; maintain clear commit history, pull requests, and versioned releases.
• Develop and maintain data models (relational, analytical, or lakehouse) that support high-quality, well-structured data.
• Lead the definition of data integration patterns and cross-domain consistency—reducing fragmentation between several business areas.
• Partner with architecture and product teams to align evolving data needs with the technical roadmap.
• Monitor, tune, and enhance data performance across ingestion, transformation, and compute layers.
• Implement best practices for quality assurance, including automated data quality checks, lineage capture, and validation processes.
• Contribute to documenting data architecture, data definitions, and engineering standards to support strong governance.
• Work closely with product teams, analysts, and data consumers to translate business requirements into scalable engineering solutions.
• Collaborate with DevOps and cloud engineering teams to ensure data pipelines (database schema deployments, version control, infrastructure-as-code, etc.) operate seamlessly within CI/CD environments.
• Lead and mentor offshore developers: delegate tasks, review code, provide feedback, run knowledge transfers, and maintain effective cross–time-zone communication.
• Collaborate with upstream source teams, QA, business stakeholders, and DevOps to coordinate schema changes, support testing/UAT, and validate deployments.
• Troubleshoot and resolve production issues and bugs; perform root-cause analysis and implement fixes and preventive measures.
• Author and maintain technical design documents, source-to-target mappings, runbooks, and operational playbooks; keep JIRA updated daily with progress, blockers, and estimates.

Benefits

• 401k Matching
• Retirement Plan
• Generous Paid Time Off