Top Tech Jobs & Startup Jobs in Denver & Boulder, CO

Owns end-to-end delivery of production data platforms for utility clients, including discovery, architecture, ingestion, transformation, orchestration, governance, quality, deployment, and handover. Uses Databricks, Spark, Python, SQL, and AWS to modernize legacy systems, integrate utility data sources, build observable pipelines, and deliver operator-facing applications. Leads client engagements, manages scope and timelines, supports migrations, and contributes reusable engineering frameworks and reference architectures.

Build and deploy production generative AI and machine learning systems for utility clients. Own engagements end to end, including discovery, architecture, retrieval, model selection, evaluation, deployment, monitoring, operator-facing applications, and post-go-live support. Integrate with client data and business systems, establish AI safety and governance controls, manage scope and risk, and communicate with technical teams and executives. Contribute reusable accelerators, evaluation frameworks, and reference architectures across client engagements.

Supports AMI and utility technology implementations through test strategy development, requirements traceability, test execution, defect resolution, data-flow validation, and data-quality reviews. Analyzes system exceptions and operational reports, validates meter and installation records, and helps establish sustainable data-management, quality-assurance, remediation, and escalation processes. Provides documentation, training, coaching, and implementation support to utility clients, vendors, integrators, and field teams.

Lead full-cycle, consultative sales for utility-focused data science solutions: nurture leads, manage complex opportunities, collaborate with SMEs and business units, drive executive-level conversations, and close deals to modernize grid reliability and customer engagement.

Design, build, and maintain scalable data pipelines, lakes, and infrastructure using Databricks, Spark, Python, and AWS. Collaborate with ML engineers, data scientists, and analysts to deliver data products, ensure data quality and governance, implement DevOps/SDLC practices, mentor junior engineers, and evaluate new data technologies.