# Sr. Data Engineer

**Company:** [Mariana Minerals](https://hotfix.jobs/companies/mariana-minerals)
**Location:** Ann Arbor, MI, Houston, TX, San Francisco, CA
**Role:** Data Engineering
**Salary:** $140k – $180k/yr
**Experience:** 4+ years
**Skills:** Python, SQL, Data Pipelines, Database Schema Design, Data Quality, Data Observability, Data Lineage, CI/CD, Containers, Time-Series Data
**Posted:** 2026-06-10

> Senior Data Engineer owning end-to-end data domains for industrial plant operations. Designs pipelines, schemas, and contracts from messy sensor/lab sources to support ML and operational decisions.

## Job Description

## 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.

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