Sr. Data Engineer

What You’ll Do

• Work across domains—for example, all plant sensor and historian data, or all lab and analytical results—including schema design, orchestration, reliability, and the contract it exposes to everyone downstream.
• Design and evolve our fleet of pipelines that pull from messy industrial sources—sensors, lab systems, historians, imagery, and more—into our databases and warehouse.
• Model time-series and analytical plant data for both human analysis and machine learning training, validation, and monitoring; own data quality, observability, and lineage in your domain.
• Build the data architecture that feeds production ML—the training and monitoring layer—in partnership with the ML engineers who own the model-specific semantics.
• Mentor earlier-career engineers and define the data contracts other teams build against.
• Work the boundary with machine learning deliberately: you own the platform and the interface it exposes; ML engineers own the features and models built on top of it. The training and monitoring layer is shared ground you design together.

Desired Qualifications

• 4+ years in data engineering or a closely related role.
• Strong Python and SQL, with deep experience designing database and warehouse schemas, including time-series and/or analytical data.
• Proven experience building reliable, orchestrated data pipelines and operating them in the cloud with containers and CI/CD.
• Experience with data quality, observability, and lineage, and comfort with messy real-world sources—drifting sensors, malformed exports, and the quirks of industrial systems.
• A self-starter comfortable in high-ambiguity environments, working directly with process engineers, ML engineers, and operations teams.
• Bonus: experience feeding data to ML systems—training datasets, feature pipelines, model monitoring—or working with industrial, sensor, or historian data.