Data Engineer

At Bio Agilytix, we are passionate about premier science and the impact it has on our world. Our team of highly experienced scientists and professionals deliver tailored services for supporting new medicine breakthroughs with best-in-class bioanalytical services. We are tirelessly committed to our customers by being solution-oriented and deadline-driven. . and we are growing. Our culture is fast-paced, fun and never boring.

Because we work across numerous clients and drug modalities, your career can develop rapidly. You’ll gain experience with a variety of challenges all while you enable life-changing, life-saving therapeutics to the patients who need them.

The Data Engineer is a hands-on technical lead responsible for designing, building, and supporting Bio Agilytix's Enterprise Data Platform. This role develops scalable, validated, production-ready data pipelines, enterprise data models, and certified data products supporting laboratory operations, scientific analytics, sponsor reporting, regulatory compliance, and AI initiatives.

Working under the direction of the Data Management Lead, and collaborating with cross-functional stakeholders, this role implements modern data engineering practices including Snowflake, dbt, dimensional modeling, semantic modeling, CI/CD automation, Data Ops and enterprise data governance standards.

• Design, develop, and support scalable enterprise data platforms that enable trusted, governed, and high-quality data products for analytics, scientific operations, sponsor reporting, regulatory compliance, and AI initiatives
• Build and maintain production-grade ELT pipelines that integrate data from laboratory information systems (LIMS), ERP, CRM, APIs, sponsor systems, cloud applications, and other enterprise data sources
• Develop modular, reusable data transformation frameworks using modern ELT practices, including automated testing, documentation, lineage, version control, and deployment automation
• Develop and implement dimensional data models, semantic models and governed datasets based on the established enterprise business definitions across the organization
• Develop validated, traceable, and auditable data pipelines supporting regulated laboratory operations, sponsor deliverables, and enterprise reporting while ensuring data integrity, reproducibility, lineage, and compliance with GxP, GLP, HIPAA, 21 CFR Part 11, and enterprise data governance standards
• Implement automated data validation, reconciliation, data quality controls, audit logging, monitoring, observability, and operational alerting to ensure reliable and trusted enterprise data
• Optimize the performance, scalability, security, governance, and operational efficiency of the enterprise data platform
• Provide operational support for the enterprise data platform through production monitoring, incident resolution, root cause analysis, and continuous reliability improvements
• Support sponsor-facing data delivery by building automated data harmonization, transformation, validation, lineage, and regulatory reporting processes
• Develop certified, governed, and AI-ready data products that support enterprise analytics, machine learning, semantic search, and generative AI initiatives
• Collaborate with Laboratory Operations, Quality teams, Information Technology, and business stakeholders to deliver scalable, reusable, and governed enterprise data solutions

• Other duties as needed

• Bachelor’s degree in computer science, Information Systems, Engineering, Mathematics, Data Science, or related field;
  Master's preferred
• 5+ years of experience in Data Engineering, Data Management, Software Engineering, Business Intelligence, or related technical disciplines, preferably within life sciences, biotechnology, pharmaceuticals, CROs, healthcare, or other regulated industries
• 3+ years of hands‑on experience designing, developing, and supporting enterprise‑scale data engineering solutions in production environments
• 3+ years of hands‑on experience architecting, developing, and administering enterprise solutions using Snowflake as a primary cloud…