Top Data & Analytics Jobs in CO

Partner with Customer Success and CS Ops to build post-sales analytics: customer health scoring, renewal and renewal pipeline reporting, billing and support signal analysis, and expansion/upsell dashboards. Own dashboard ecosystem, translate stakeholder requests into scalable reporting, collaborate with Finance and Data Engineering, and leverage AI tools to accelerate analytic workflows and maintain data quality for retention and expansion decision-making.

Analyze large datasets for the Fraud and Risk team, develop dashboards and reporting structures, present insights, and support data-driven risk prevention. The role involves quantitative research, fraud detection, risk assessment, rules-based and machine learning models, and collaboration with stakeholders to solve business problems.

Analyze large datasets for the Fraud and Risk team, develop dashboards and reporting structures, present insights, and support data-driven risk prevention. The role involves quantitative research, stakeholder communication, and assisting with rules-based and machine learning models to detect fraud, assess risk, and improve compliance and security measures.

Analyze large datasets for fraud prevention and risk management, develop dashboards and reporting structures, present quantitative research, and support rules-based or machine-learning models. Collaborate with management and cross-functional stakeholders to identify business problems and implement data-driven solutions that improve risk prevention and compliance.

Designs and maintains production data pipelines, dbt models, Airflow workflows, and Snowflake datasets integrating Workday and other People systems. Owns data quality, observability, governance, row-level security, and access controls for sensitive People data. Builds semantic models and AI agents, collaborates with analysts and stakeholders, translates requirements into reliable data products, communicates technical trade-offs, and mentors other engineers.

Leads and develops a data engineering team supporting People Analytics and AI-assisted workforce analytics. Designs scalable data models, pipelines, semantic layers, testing frameworks, and governance practices. Remains hands-on with production code, reviews, and technical problem-solving while partnering with People Analytics, business, legal, and compliance stakeholders. Establishes engineering standards across Python, SQL, dbt, Airflow, Snowflake, CI/CD, data quality, observability, privacy, and security.

Design, build, and optimize modular dbt pipelines and data models; translate ambiguous product requirements into production-grade data assets; diagnose query performance and lower warehouse compute costs (including migration to Snowflake); enforce code hygiene, data security, testing, and peer review; mentor junior engineers and adopt AI-assisted development tools to speed test generation and legacy discovery.

Analyze administrative and clinical healthcare data to build reporting solutions, identify optimization opportunities, and support clinical program effectiveness. Apply statistical analysis, data mining, and predictive modeling to improve decision-making, patient outcomes, utilization, and cost efficiency. Develop dashboards and KPIs, monitor healthcare trends, collaborate with clinical, data science, engineering, and client teams, and lead complex multidisciplinary projects.

Design, build, and lead scalable data platforms, pipelines, data products, and ELT/ETL processes supporting analytics and AI/ML workloads. Establish standards for data quality, testing, monitoring, observability, CI/CD, security, and reliability. Own projects through deployment and production support, collaborate across technical and business teams, use AI-assisted engineering tools, and mentor data engineers.

Lead the architecture and development of scalable AWS-based data ingestion, transformation, and orchestration pipelines. Build reliable data infrastructure using Python, SQL, Airflow, Lambda, ECS, SQS, and Terraform. Establish data modeling, quality, lineage, monitoring, testing, and observability practices while partnering with analysts, scientists, and backend engineers. Mentor senior engineers, guide technical decisions, and provide hands-on leadership for complex data platform initiatives.

Build and lead Ashley’s consumer insights capability, including research roadmaps, custom qualitative and quantitative studies, insights repositories, prioritization processes, and executive-ready storytelling. Translate consumer, retail, marketplace, feedback, and business data into actionable recommendations for product, marketing, merchandising, pricing, and growth decisions. Influence cross-functional stakeholders, develop AI-enabled synthesis workflows, and manage one direct report while operating resourcefully with imperfect data.

Lead and manage delivery of modern data ecosystems and managed data analytics programs. Oversee project planning, budgeting, quality, and client accounts. Mentor teams, drive data strategy, convert insights to business opportunities, and ensure standards and risk management across large transformations.

Build, deploy, and operate production-grade data pipelines and data products for healthcare audiences. Design transformations, data models, and governed views using Python, SQL, Airflow, S3, Snowflake, and EMR. Implement data-quality, monitoring, and privacy-by-design controls for PHI/PII. Partner with product, analytics, and platform teams to onboard sources, support audience discovery, segmentation, activation, measurement, and troubleshoot production issues.