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

Provides data-driven strategic insights and business intelligence solutions for clients. Responsibilities include analyzing complex datasets, developing reports and dashboards using Oracle BI, QlikView, and BIRT, delivering recommendations, managing stakeholders, building client relationships, and collaborating with teams to optimize business performance.

Own Eve’s data platform end to end, including ingestion, orchestration, incremental dbt models, Snowflake administration, Terraform infrastructure, observability, access controls, and pipeline reliability. Build schema-change detection, freshness SLAs, CI environments, and incident processes while managing performance and compute costs. Partner with analytics engineers and stakeholders to maintain trusted data for reporting and AI systems, using AI-assisted development and documenting platform standards.

Staff Data Engineer will set technical direction and build Eve’s scalable data platform. Responsibilities include Snowflake administration, role-based access, governance, ingestion, dbt modeling and orchestration, Terraform infrastructure, observability, schema change detection, reliability practices, and CI standards. The role also mentors engineers and analysts, supports AI-assisted development, and establishes secure, reliable workflows for product, case, customer, and go-to-market data.

Manages programmatic analytics and insights for client accounts, leading measurement frameworks, brand lift studies, incrementality testing, and A/B experiments. Develops media and campaign strategies, communicates performance insights to clients, and supports pitches and QBRs. Oversees, mentors, and develops analysts while partnering with Product, Engineering, Sales, and Customer Success teams to improve measurement tools, campaign optimization, and analytical capabilities.

Lead firmwide data governance initiatives focused on data quality, controls, data contracts, lineage, and regulatory compliance. Develop governance frameworks, policies, operating models, and ownership structures; oversee implementation across critical data elements; partner with data, product, technology, and architecture teams; lead governance forums; resolve complex data issues; and drive change management and continuous improvement.

Designs and leads enterprise data architecture modernization across cloud and lakehouse environments. Responsibilities include data modeling, Medallion architecture, storage tiering, vector databases, RAG, semantic layers, query and compute optimization, governance, access controls, lineage, integration, and compliance. The role partners with architecture, engineering, and business teams to deliver scalable, secure, highly available data assets and supports AI initiatives.

Leads global contact center capacity planning, forecasting, headcount, and hiring strategies. Builds predictive staffing models using Python, analyzes contact flows and staffing accuracy, and develops reports with MicroStrategy, Alteryx, and Power BI. Communicates data-driven recommendations to executives, partner managers, vendors, and operations teams while managing planning initiatives and optimizing service levels, customer experience, and costs.

Design, build, and operate scalable batch and streaming data pipelines, lakehouse and warehouse models, and data services. Own datasets end to end, improve Snowflake, Iceberg, Spark, and Flink performance and reliability, implement governance and observability, and support graph-serving data models. Partner with product and engineering teams, participate in on-call, review code and designs, and mentor junior engineers.

Analyzes broadband, customer, product, and marketing data to evaluate campaign performance, acquisition, retention, segmentation, channel effectiveness, and ROI. Develops dashboards, reporting frameworks, analytical models, and executive presentations. Partners with Marketing, Product, Data Science, Finance, Sales, and Technology teams to define KPIs, measurement approaches, and actionable recommendations that improve business performance and customer experience.

Leads the design, development, implementation, and support of scalable data solutions across multiple applications and platforms. Responsibilities include data requirements analysis, ETL/ELT development, data modeling, migration and transformation, workflow orchestration, performance optimization, data quality, pipeline monitoring, and production support. The role also provides technical leadership through coding standards, code reviews, mentorship, and engineering best practices.

Owns fraud performance analytics for depository products, including monitoring losses, approval rates, false positives, customer friction, and risk metrics. Builds data models, transformation pipelines, dashboards, scorecards, and monitoring frameworks. Conducts fraud deep dives and develops optimized rules, policies, thresholds, models, and decision strategies. Partners with Product, Engineering, Machine Learning, Data Engineering, Fraud Operations, and Risk teams to improve fraud detection and support product launches.