Top Data & Analytics Jobs in CO

Build and own end-to-end compliance data pipelines and models (case, alert, entity, transaction, risk features). Ensure data quality, monitoring, lineage, and audit-ready documentation. Partner with Compliance and engineering to close upstream gaps, improve detection signal quality, and implement measurement frameworks and reporting for control effectiveness.

The Senior Customer Lifecycle Analyst leads data-driven projects focusing on customer retention, engagement, and lifetime value, leveraging advanced analytics and visualization tools to provide business insights and recommendations.

Lead automation and measurement of the AI Governance program by building self-service dashboards, tracking Time-to-Approval and throughput, analyzing trends to remove bottlenecks, producing C-suite Evidence Packs, recommending workflow improvements, and collaborating with delivery analysts to increase governance maturity and operational throughput.

Analyze transactional and operational payments data to monitor performance, detect risk and fraud, and improve collections. Build, validate, and automate daily/weekly/monthly reports and alerts, consolidate data sources, deliver insights to stakeholders, and support special analytics projects to drive operational efficiency.

Deliver data engineering and ETL solutions to support client analytics. Build and maintain data pipelines, optimize SQL, design data architecture (star/snowflake models), create BI reports/dashboards, improve data quality, and support client engagements in regulated environments while learning and growing technical and client-facing skills.

Lead data engineering engagements to design, build, and maintain ETL/ELT pipelines and cloud data architectures. Manage client accounts and mentor teams, leverage tools like DataStage, AWS/Redshift, DB2/SQL Server, GoldenGate, and BI/visualization platforms to deliver analytics, performance tuning, and scalable reporting solutions.

The Senior Manager of Data Engineering will build a data platform, lead a team, and ensure data accessibility to support AI and business operations.

Develop and maintain data contracts, metadata standards, AI-readiness scoring pipelines, and governance/compliance tooling. Build quality monitoring, semantic layer assets, and AI-augmented development workflows. Collaborate with data owners and platform teams to onboard assets, ensure data trust, and enable AI-driven analytics.

Build and maintain BI solutions: design ETL and pipelines, create dashboards and reports, automate large-scale data transfers, ensure data quality, and deliver actionable analyses to marketing stakeholders and senior leadership.

Lead Finance Data & Analytics to build trusted data sources, governance, and analytics for Finance and cross-functional stakeholders. Manage analysts, drive data strategy and Snowflake data models, produce insights via SQL and Tableau, and deploy AI solutions to optimize finance decision-making and operations.

Lead Data Analyst responsible for advanced analytics, real-time dashboards, capacity modeling, and network performance insights. Collaborates with stakeholders to identify inefficiencies, run tests and pilots, and deliver actionable recommendations to improve service provider performance and network operations.

Design and build Toast’s enterprise analytics layer and AI-ready semantic layer in the data warehouse. Collaborate with domain teams and data engineering to implement hub-and-spoke architecture, define complex business logic in SQL, support scalability, partner with stakeholders, and provide technical mentorship.

Manage design and delivery of reusable, AI-ready data products for retail-banking portfolios. Develop interoperable data-engineering artifacts, enable self-service analytics, maintain data marketplace, oversee data policies, integrity, budgeting, stakeholder alignment, and lead cross-functional teams in a matrixed environment.

Design, harden, and scale Snowflake and AWS data platforms, build dimensional data models and resilient Airflow pipelines, optimize complex SQL, develop dbt semantic layers, deploy ML models in AWS, integrate AI/LLMs into workflows, and build data APIs (FastAPI/Flask) to enable enterprise analytics and operational ROI.