Top Senior Data & Analytics Jobs in Denver & Boulder, CO

Analyze and transform cloud data using SQL, dbt, and Google Cloud; build automated pipelines, data visualizations, validation checks, unit tests, monitoring, and alerts. Partner with engineering teams and enterprise insurance clients to improve data quality, governance, automation, and decision-making. Communicate insights clearly, document technical work, develop insurance domain expertise, and contribute to collaborative reviews and on-call support.

Designs, develops, and supports analytics data solutions for clients, including Snowflake and dbt ingestion and transformation pipelines, data marts, validation, monitoring, governance, and documentation. Manages multiple consulting projects, budgets, timelines, quality assurance, troubleshooting, client communication, and implementation planning. Requires strong data engineering experience, regulated-industry knowledge, stakeholder communication, and willingness to travel.

Build scalable, secure data pipelines, models, platforms, and governance systems that enable AI applications to access trusted enterprise data. Partner with analytics, marketing, and product teams to translate requirements into technical solutions, independently deliver projects through production, and support sensitive-data security and access controls. Preferred experience includes LLM architectures, cloud data platforms, infrastructure as code, regulated environments, and technical leadership.

Design and scale lakehouse architecture, including Bronze, Silver, and Gold data layers. Build reliable streaming and batch pipelines with Kafka, Spark, and Airflow; manage Iceberg, Delta, and Hudi table formats; optimize Trino, Starburst, and Databricks queries; and improve platform reliability, data quality, and performance. Collaborate with data scientists, analysts, and product teams to deliver scalable data platform solutions.

Leads enterprise data and analytics strategy, architecture, modernization, and delivery across cloud and on-premises environments. Oversees data platforms, engineering, integration, governance, quality, lineage, security, and compliance. Partners with executives to define roadmaps and investments, manages cross-functional teams and vendors, enables AI/ML capabilities, and mentors data professionals while driving scalable, trusted data solutions and measurable business outcomes.

Designs and maintains scalable data pipelines, integration workflows, data lakes, and engineering platforms using AWS, InterSystems Ensemble, HL7, CCDA XML, and MySQL. Responsibilities include transforming and validating clinical data, optimizing queries, supporting analytics, solving complex client data challenges, and mentoring junior engineers. The role also involves client collaboration, critical thinking, Agile delivery, and adherence to professional and technical standards.

Build AI-augmented BI dashboards, semantic layers, and AI-ready metadata. Use AI-assisted tools to accelerate SQL and dashboard development, productionize ML outputs into BI, enforce data quality and governance, and present insights to business stakeholders including C-suite.

Own the company’s metric registry and canonical definitions, resolve conflicting metrics, enforce definitions in semantic and catalog systems, and retire redundant reporting assets. Build data quality checks, route failures to accountable owners, and evaluate AI agents against ground truth. Enable trustworthy self-service analytics through governed data. The role requires strong SQL, production ownership of a semantic or metrics layer, data catalog or lineage experience, clear documentation, and cross-functional decision-making.

Own end-to-end data products, including source onboarding, API and file-feed ingestion, orchestration, incremental and idempotent pipelines, Snowflake and dbt Cloud modeling, testing, documentation, alerting, and production reliability. Establish engineering standards, manage secrets and governance, use AI coding tools responsibly, collaborate with vendors and stakeholders, and mentor junior engineers.

Develop and maintain dashboards, reports, scorecards, KPI solutions, and analytical models that provide business performance insights. Responsibilities include trend and root-cause analysis, forecasting, data validation, stakeholder requirements gathering, reporting automation, process improvement, documentation, and data quality governance. The role partners with cross-functional teams and communicates findings to technical and non-technical audiences.