Role SummaryThe Data Engineer, Operations Analytics builds the data platform, pipelines, data standards, and quality controls that make the team's data reliable. Working alongside the Data Scientist, this role owns the data from the agreed handoff point through to curated, analysis-ready tables. The role reports to the Director, Operations Analytics.
This role partners closely with the Data Scientist, IT, vendor partners, Operations, and Compliance to make sure reliable data is in place before the first turnkey solution in mid-2027, which will add new data sources and users. As the team grows, it also gives the Reporting Lead and Business Analysts documented standards and curated tables to build on. As a second owner of the analytics workspace, the position reduces dependence on any one person, keeps the platform supportable through shared change control and documentation, and supports audit readiness. It also prepares and governs data for AI and language model use cases.
Travel: Ability to travel up to 25% for company gatherings or to project sites.
Location: Denver, Colorado (3-day hybrid in-office role)
Compensation: $108K - $125K (Offers Bonus)
Essential Responsibilities- Build and evolve the team's data platform on Azure and Databricks, including ingestion, storage, and curated layers
- Govern the workspace through Unity Catalog, including catalog and schema design, permissions, lineage, and data classification, so access is controlled and auditable as new users and data sources come online
- Own ingestion into Databricks from the agreed handoff point with vendor partners and IT, and review upstream building and facility data platform work
- Design, build, test, and monitor production pipelines for power, cooling, equipment, and monitoring data, and scale them as new data sources and users come online
- Define and maintain a reference data standard and data dictionary, including consistent naming for sites, assets, equipment, and vendors, documented so new team members can onboard quickly
- Build data quality checks, monitoring, alerts, and runbooks so issues are detected and resolved quickly
- Publish and maintain curated tables that serve models, Tableau scorecards, and KPIs so every report and model draws on the same trusted data
- Partner with the Data Scientist to build and maintain Genie spaces and LLM-powered tools, including the curated tables, metadata, table and column descriptions, and certified queries they depend on
- Prepare and maintain data for AI and language model use cases, such as document and text ingestion, vector search indexes, and retrieval pipelines, held to the same quality and change control standards as other production data
- Serve as second owner of the Databricks workspace and co-own change control with the Data Scientist, including code review and separate development and production environments
- Partner with the Data Scientist, Operations, Reliability, Engineering, and IT teams, and with the Reporting Lead and Business Analysts as they join, to align data structures with modeling, reporting, and operational needs
- Create repeatable exports of platform evidence, such as access and activity logs, to support audits and the Compliance Analyst
Education, Skills, and Experience- Bachelor's or Master's degree in Computer Science, Information Systems, Engineering, or a related field, or equivalent practical experience
- 3+ years of experience in data engineering, analytics engineering, or a related data platform role
- Hands-on experience building data platforms and operating production pipelines in Databricks using SQL, Python, and Spark
- Experience with Azure data and cloud services, including storage, networking, and identity concepts
- Experience using AI-assisted development tools such as Claude Code to build, review, and document code
- Experience with data modeling, data quality monitoring, and change control practices such as code review and separate development and production environments
- Experience with complex operational datasets, including time-series or telemetry data
- Ability to document standards clearly and communicate with technical and non-technical stakeholders
- Experience building data pipelines for AI and LLM applications, such as document parsing and chunking, embedding generation, vector search indexes, and retrieval evaluation
- Preferred familiarity with Databricks, AI/BI Genie, Vector Search, Model Serving, Tableau, and Airtable
- Preferred experience with data center, industrial, or building systems data
Fueled by Excellence & CollaborationAt Rowan, we are a distributed team united by a common mission to transform data center delivery. Across our two offices, we offer a hybrid work environment supported by teamwork, technology, and in-office gathering time to help you be successful. We are headquartered in downtown Denver, Colorado and have an office in Seattle, Washington. Our team of talented and passionate individuals is spread out across the U.S.
Here to support youAt Rowan, we want to make sure you can do great work. We offer competitive benefits focused on helping you be and achieve your best.
- Hybrid working environment
- Team building and educational opportunities
- Generous benefits package including robust health benefits and a 401(k)-company contribution
- RTD ECO pass or parking reimbursement
- Competitive compensation, bonus, and an incentive plan
- Relocating for this role? We offer relocation assistance on a case-by-case basis to help make your transition smooth
