Data Engineer, Operations Analytics

Rowan Digital Infrastructure • Denver, CO

23h • $109,000 - $125,000 • Hybrid

About The Position

The Data Engineer, Operations Analytics builds and maintains the data platform, pipelines, data standards, and quality controls that make operational data reliable. Working alongside the Data Scientist, this role owns data from the agreed handoff point through to curated, analysis-ready tables and reports to the Director, Operations Analytics.

The position partners with the Data Scientist, IT, vendor partners, Operations, and Compliance to establish reliable data ahead of the first turnkey solution in mid-2027, which will add data sources and users. It provides documented standards and curated tables for the Reporting Lead and Business Analysts, shares ownership of the analytics workspace to support continuity and audit readiness, and prepares and governs data for AI and language model use cases.

This is a hybrid role based in Denver, Colorado, with three in-office days per week and travel of up to 25% for company gatherings or project sites.

Requirements

• Bachelor’s or Master’s degree in Computer Science, Information Systems, Engineering, or a related field, or equivalent practical experience.
• At least 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.

Nice To Haves

• Familiarity with Databricks, AI/BI Genie, Vector Search, Model Serving, Tableau, and Airtable.
• Experience with data center, industrial, or building systems data.

Responsibilities

• Build and evolve the team’s data platform on Azure and Databricks, including ingestion, storage, and curated layers.
• Govern the Databricks workspace through Unity Catalog, including catalog and schema design, permissions, lineage, and data classification, to keep access controlled and auditable.
• 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 sources and users grow.
• Define and maintain reference data standards and a data dictionary, including consistent naming for sites, assets, equipment, and vendors.
• Build data quality checks, monitoring, alerts, and runbooks to detect and resolve issues quickly.
• Publish and maintain curated tables for models, Tableau scorecards, and KPIs so reports and models use trusted, consistent data.
• Partner with the Data Scientist to build and maintain Genie spaces and LLM-powered tools, including their curated tables, metadata, table and column descriptions, and certified queries.
• Prepare and maintain data for AI and language model use cases, including document and text ingestion, vector search indexes, and retrieval pipelines, using production-quality standards and change control.
• Serve as a 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, as well as the Reporting Lead and Business Analysts, 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.

Benefits

• Hybrid working environment.
• Team-building and educational opportunities.
• Generous benefits package including robust health benefits and a 401(k) company contribution.
• Unlimited PTO.
• RTD ECO pass or parking reimbursement.
• Competitive compensation, bonus, and an incentive plan.
• Relocation assistance on a case-by-case basis.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level