Data Engineer Jobs in Durham, NC

Build and maintain data pipelines integrating laboratory instruments, automation systems, operational platforms, APIs, robotics, databases, and files. Develop SQL transformations, data models, validation checks, orchestration, monitoring, and alerting to support analytics, dashboards, troubleshooting, and AI systems. Document infrastructure and datasets for regulatory compliance while collaborating with engineering, laboratory operations, data science, and automation teams. This is an on-site role with on-call and occasional weekend or holiday support.

Design, build, and operate end-to-end ELT data pipelines on Azure/Databricks. Implement DLT data quality checks, CDC, schema evolution, dimensional models for Power BI, monitoring, CI/CD, and operational runbooks while partnering with architects and BI stakeholders.

Design, build, and maintain BI-ready datasets, dashboards, and monitoring tools for high-throughput lab operations. Implement process monitoring (SPC), ensure data quality and audit readiness, perform root-cause analysis, and partner with Data Science to deploy predictive models that improve throughput, quality, and compliance.

Leads enterprise data architecture and engineering initiatives, building scalable cloud data lakes, pipelines, warehouses, and integration frameworks. Designs dimensional models, ETL/ELT patterns, governance practices, and analytics solutions using Snowflake, Azure, AWS, and big data technologies. Oversees testing, QA readiness, documentation, project planning, risk management, and delivery across multiple platforms. Collaborates with business and analytics teams, advises senior leadership, resolves technical issues, and mentors junior engineers.

Designs and implements cloud-first, event-driven data solutions and ETL pipelines for data warehouses (Snowflake/Oracle). Leads architecture, CI/CD, QA, and Agile delivery; mentors teams; builds scalable backend systems using AWS, APIs, and automation tools. Ensures project delivery on time and within budget while advising senior management on technical strategy.

Design, build, and support a validated enterprise data platform and production-grade ELT pipelines using Snowflake and dbt. Deliver governed, auditable, AI-ready data products for laboratory operations, sponsor reporting, regulatory compliance, and analytics while enforcing data quality, security, lineage, and observability.

Build, operate, and enhance Banc of California’s Azure- and Snowflake-based data platform. Develop batch, CDC, and streaming ingestion pipelines; optimize Snowflake performance; support dbt workloads; implement CI/CD, DataOps, monitoring, automation, and disaster recovery. Provide Tier 2 production support and administer tools including Power BI, Purview, and Alteryx. Collaborate with data engineering, architecture, BI, and business teams to deliver secure, reliable, governed data solutions.

Own and improve approximately 150 RESTful performance-data integrations supporting Teamworks’ Athlete Management System. Maintain C#/.NET integrations, investigate vendor API issues, strengthen reliability, resilience, observability, documentation, and security, and provide on-call support. Lead migration of integrations to Python and a modern lakehouse architecture deployed in AWS, including environments supporting military customers.

Lead the design, development, optimization, and maintenance of scalable batch and streaming data pipelines. Build ETL/ELT workflows using Python, SQL, and AWS services; optimize data models and cloud data platforms; ensure data quality, integrity, reliability, and performance; establish CI/CD and infrastructure-as-code practices; resolve pipeline issues; document systems; and mentor junior engineers.

Design, build, and modernize full‑stack web applications and microservices for a real‑time event-driven data and visualization platform. Develop and optimize streaming data pipelines (Kafka, Flink, Spark), integrate observability, caching (Redis), and AI agents, and collaborate with hardware verification teams for scalable, low-latency solutions.

The Software Engineer will be responsible for data collection and management, enhancing the cloud infrastructure, and collaborating on AI datasets to support model training.