Top Data & Analytics Jobs in CO

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.

The Reports Analyst prepares ad hoc and scheduled reports, responds to data requests, analyzes trends, and provides recommendations to business management. Responsibilities include researching and downloading data, creating presentations and exhibits, manipulating spreadsheet and database data, maintaining databases, and ensuring data integrity and accurate reporting. The role requires advanced Excel skills, reporting and data analysis experience, strong communication, and effective use of AI tools.

Leads data modernization initiatives for healthcare payer clients, designing pipelines, integrations, transformations, architectures, and warehouse solutions. Manages complex delivery using Airflow, Azure Data Factory, Databricks, and Snowflake; improves data quality, governance, security, and performance. Advises clients on data strategy and modernization roadmaps while coaching technical teams and aligning solutions with business, reporting, compliance, and security requirements.

Analyzes operational and vehicle inspection data, builds and maintains reports, dashboards, queries, metrics, and data processes, and delivers insights to Operations and leadership. The role cleans and transforms large datasets, validates data quality, supports data science and engineering teams, develops monitoring systems, and presents trends and recommendations for business decisions. It also partners with product, dispatch, quality assurance, and technology teams on operational data and reporting needs.

Business Intelligence Interns will conduct exploratory data analysis, create ad hoc reports, and support the development, migration, and improvement of dashboards using Looker and Databricks. They will gather requirements, build visualizations, test dashboard accuracy and usability, identify data inconsistencies, and document findings while collaborating with team members on business initiatives.

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.

Lead design and implementation of scalable Databricks Lakehouse solutions using Delta Lake, Unity Catalog, and MLflow. Advise stakeholders, drive cloud data platform modernization across Azure/AWS/GCP, ensure data governance and quality, optimize performance and cost, and mentor teams. Support presales, deliver billable consulting, and champion AI/ML-enabled data solutions to translate platform capabilities into business outcomes.

Build and operate data platform services, APIs, SDKs, schemas, validation tools, event-driven pipelines, and reverse ETL workflows. Improve Kafka-based data delivery, schema evolution, observability, lineage, access controls, privacy, and compliance. Partner with engineering, analytics, data science, security, and infrastructure teams to create reliable, self-service data workflows. Operate AWS services, infrastructure as code, monitoring, alerting, and incident response.