- Data Engineer
Contract 40 hours weekly, 12+ months
This is a Flex position with the potential to convert to a regular full-time position based on business needs, individual performance, and organizational priorities.
In this role, you will help shape the data foundation that supports research and development activities across Source Research. Working closely with lab owners and experimental, modeling, and ML scientists, you will build and improve data pipelines, integrate diverse data sources, and enable reliable access to research data at scale. You will also help establish practical architecture standards and best practices that ensure our data platform remains scalable, secure, maintainable, and aligned with the broader data landscape.
his role combines hands-on development with technical leadership in shaping the data foundation for Source Research. You will build, operate, and continuously improve data pipelines, integrating new data sources, improving reliability, and enabling scientists and engineers to use high-quality data at scale. You will also define practical architecture standards that keep the platform consistent, secure, future-ready, and aligned with clients data landscape.
- Work independently and collaboratively to deliver on objectives, whether exploring new data sources, building new capabilities, or characterizing existing system performance.
- Be willing to work extended hours and second shift as needed.
- Perform other duties as assigned or required
Qualifications• Bachelor's or Master's degree in Computer Science, Statistics, Math, Data Science, or a related field.
• 10+ years of relevant experience in data engineering, data architecture, or scientific/engineering data platforms.
• Strong hands-on development experience in Python and modern data engineering tooling.
• Proven experience building and operating scalable big data pipelines, analytics platforms, and data products that support data-intensive scientific and engineering workflows.
• Experience with cloud and distributed data platforms such as Azure, Azure Databricks, Apache Spark, Kubernetes, and data lake architectures.
• Solid understanding of data modeling, metadata management, data lineage, data quality, governance, security and access control.
• Experience supporting scientific or engineering workflows (for example simulation, HPC, instrumentation, or time-series sensor data).
• Familiarity with AI/ML workflows and MLOps practices is a plus.
• Strong communication and collaboration skills, with the ability to translate technical details into clear and actionable guidance.
