Top Data Engineer Jobs in Denver & Boulder, CO

Design, build, and maintain scalable ETL/data pipelines for Comcast's national locates program. Ingest and transform data from multiple internal and external sources, optimize performance and cost, ensure data quality and governance, collaborate with analysts and stakeholders, troubleshoot pipelines, and document data structures and processes.

Design, implement, and manage advanced data ecosystems and scalable ETL/ELT pipelines. Analyze complex problems, deliver data warehousing and governance solutions, mentor junior staff, and build client relationships while maintaining high standards and business-context awareness.

Lead design and implementation of data architecture, ETL/ELT pipelines, data models, warehouses and lakes. Ensure data governance, security, cloud deployments, performance and cost optimization. Coach and manage team members, partner with stakeholders, and drive data-driven solutions for clients.

Lead design and implementation of enterprise data architecture and cloud-based data solutions. Manage large data engineering projects, develop data models and pipelines, ensure governance and security compliance, advise clients strategically, and build high-performing inclusive teams.

Design and implement data architecture and scalable ETL/ELT pipelines on cloud platforms (AWS/Azure/GCP). Analyze complex problems, mentor junior staff, ensure data governance/security compliance, liaise with stakeholders, and deliver high-quality client solutions while developing technical and client-facing skills.

Develop and maintain data pipelines and ETL processes, optimize SQL and data architectures, build BI reports and dashboards (Oracle BI, QlikView, Spotfire), analyze data to produce strategic insights, support client engagements in regulated environments, and apply continuous process improvements to ensure data quality and integrity.

Build, model, and maintain scalable BigQuery-based data solutions on GCP. Implement performant data models, storage partitioning/clustering, ETL improvements, data validation, and documentation. Collaborate with architects, data scientists, and engineers to deliver governed, high-quality data for reporting and AI initiatives.

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.

As the founding engineer of the Data Intelligence team, you will design semantic layers, develop AI tooling, and automate data processes to drive business insights.

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.

Build and maintain scalable, governed data pipelines from ingestion through transformation and integration. Implement schema validation, data quality checks, job monitoring, and production-grade APIs. Work with Python, SQL, Airflow, dbt, AWS (EMR, Athena, S3), Iceberg/Parquet, and contribute to CI/CD, observability, and on-call support while collaborating with analytics and product teams.

Lead design and operation of large-scale data pipelines and data APIs. Build Spark/PySpark workflows on Databricks, optimize job performance, manage data quality and observability, and develop MCP servers and AI-agent integrations. Mentor engineers, define standards, support production incidents and on-call rotations, and collaborate with stakeholders to deliver scalable data platform products.

Build and maintain HR data pipelines and warehousing (Snowflake), create data objects for Python and Tableau, use AI automation to clean large datasets, deliver actionable people analytics, consult with HR and executives, and manage multiple high-stakes analytics projects to inform workforce strategy and OKRs.

Lead architecture, systems, and roadmap for Coinbase's Compliance Reporting Data Platform. Design and build production-grade data models, pipelines, quality frameworks, validations, and certification processes. Establish data contracts, monitoring, reconciliation, and audit-ready processes. Partner with engineering and compliance stakeholders, respond to regulatory exams, and mentor engineers while shaping standards and documentation.

Design, build, and operate large-scale data platforms and Spark/PySpark pipelines. Enable data integration, modeling, quality, and observability. Build MCP servers and AI-augmented tooling, mentor engineers, and lead cross-functional projects to deliver reliable data products.

Design, build, and maintain scalable Spark-based ETL pipelines and computed tables in a central data lake. Integrate structured and unstructured IoT, sensor, and external data for analytics, model training, and dashboards. Collaborate with Data Science, Analytics, and ML teams to ensure reliable, high-quality customer-facing datasets.

Build and maintain cloud cost monitoring, alerting, and optimization tooling. Develop automation, cost allocation/tagging, data pipelines, dashboards, and integrations with billing APIs. Support FinOps processes, implement cost guardrails, troubleshoot cost systems, participate in on-call rotation, and collaborate with senior engineers and product teams to drive cost-efficient infrastructure and reporting.

Design, build, and maintain scalable ETL and data pipelines on AWS using Python/PySpark. Develop Glue/Lambda/Step Function workflows, event-driven integrations, and data lake/warehouse solutions. Tune SQL, implement monitoring/validation, support API/JSON integrations, troubleshoot operations, document and automate deployments, and mentor junior engineers.

Support building and maintaining ETL pipelines using Python and PySpark. Work with AWS services (Glue, Lambda, Step Functions, S3, Redshift, RDS, EMR) to ingest, transform, and validate data. Write complex SQL, implement basic monitoring/logging, process API/JSON payloads, and collaborate with engineers, analysts, and stakeholders on deployment and documentation.

Design and implement scalable data pipelines and orchestration on GCP to support AI/ML-driven mail intelligence. Build stream and batch processing, ensure data governance and quality, optimize workflows, create visualizations, and collaborate with US cross-functional teams to align technical strategy and recover from failures.

Design, develop, maintain, and optimize ADABAS database structures and data solutions. Support mainframe data processing (Natural, COBOL, JCL), perform migrations to modern platforms, tune performance, ensure data integrity/security, troubleshoot issues, and collaborate with developers, architects, and analysts on modernization, API/cloud integration, and data warehousing initiatives.

Lead design and delivery of scalable data lakes, warehouses, and pipelines. Define governance, quality, monitoring, and CI/CD. Build real-time streaming systems and production AI/ML data flows (embeddings, vector stores, RAG, feature stores). Mentor engineers, set platform strategy, and drive data platform modernization.

Design, build, and maintain scalable, fault-tolerant data pipelines and architectures for analytics and ML. Integrate and model large datasets, ensure data quality and governance, instrument event streaming, and work with cloud big-data platforms to enable fast, reliable insights across the enterprise.

Design, build, maintain, and optimize data infrastructure (databases, data lakes, warehouses) and ETL/ELT pipelines. Ensure data quality, security, performance, and compliance. Collaborate with analysts and stakeholders, document policies, and support monitoring, backup, and recovery strategies.

Build and maintain large-scale data processing pipelines using Apache Spark (PySpark/Scala) and Python. Design ETL workflows, implement distributed data processing, use version control (Git), and work with cloud platforms (GCP/AWS/Azure).