Top Senior Data Engineer Jobs in Denver & Boulder, CO

Design, build, and maintain scalable data pipelines, lakes, and infrastructure using Databricks, Spark, Python, and AWS. Collaborate with ML engineers, data scientists, and analysts to deliver data products, ensure data quality and governance, implement DevOps/SDLC practices, mentor junior engineers, and evaluate new data technologies.

Design, build, and optimize Snowflake-based data warehouses and dbt transformation pipelines. Develop scalable ETL/ELT ingestion, tune SQL and warehouse performance, implement data quality/testing (dbt tests), and maintain CI/CD and version-controlled data workflows while collaborating with analysts, data scientists, and stakeholders.

Lead graph data engineering to design, implement, and scale Neo4j-based graph platforms. Build graph models, ingestion pipelines (batch and streaming), integrate graphs with analytics/ML, establish governance, and enable graph-driven business use cases like fraud detection and Customer 360.

Lead design, build, and operate real-time, Flink-based streaming data platforms. Develop low-latency, stateful pipelines, enforce event schemas/contracts, ensure fault tolerance and observability, mentor engineers, and partner with architecture and compliance teams to deliver resilient streaming solutions for banking use cases.

Lead architecture and implementation of a large-scale analytics stack: design foundational pipelines, establish modeling/orchestration standards, integrate AI-assisted engineering tools, translate business questions into version-controlled data models, partner with stakeholders to define metrics and data contracts, migrate logic into maintainable pipelines, and optimize cloud warehouse performance for scale.

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.

The Data Engineer role involves developing and maintaining data pipelines for forecasting in commercial real estate, ensuring data integrity and supporting analytics teams.

The Data Engineer is responsible for designing data workflows and pipelines, implementing systems engineering principles, and deploying data streaming technologies while ensuring compliance with industry regulations and best practices.

Forward-deployed senior data engineer who deploys, hardens, integrates, and operates a data platform (lakehouse, streaming, governance) in DoD environments. Build pipelines across classification boundaries, produce accreditation evidence (STIG/cATO/FIPS), run day-2 operations, train mission teams, and mentor junior FDEs while feeding field feedback into product improvements.

The Data Engineer will design and maintain data pipelines, enhance the data warehouse, support machine learning applications, and collaborate with various teams to leverage data effectively.

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.

Owner of data platform modernization: design and migrate legacy systems to Kimball-style dimensional models and star schemas; build reliable production data pipelines; enforce data hygiene and validation; champion dbt best practices; mentor junior engineers; collaborate with analysts and stakeholders to ensure scalable, well-documented data models and platform observability.

Design, build, optimize, and modernize enterprise Microsoft data platforms (Azure, Fabric). Develop ETL/ELT pipelines, data warehouses/lakehouses, implement governance, security, DevOps/CI-CD, and support Power BI and AI initiatives while consulting with stakeholders.

Lead data engineering for a healthcare scheduling platform rebuild: map legacy multi-TB schema, design target schema, build translation layer (views/materialized views and write mappers), ensure data consistency during parallel systems, plan per-tenant cutover with verification and rollback, tune DB performance, instrument data quality/lineage/audit, and mentor engineers.

Design, build, and scale reliable batch and real-time data pipelines and warehouses. Partner with engineering, data science, and business teams to define data models, improve observability, optimize storage and querying, and evangelize data engineering best practices across the company.

Design, build, and maintain scalable Databricks-based data pipelines and ELT workflows; develop and document dbt models; define data modeling methodology; ensure data quality, reliability, and scalability; collaborate with clients and cross-functional teams; provide technical guidance and deliver improvements to clients' data infrastructure and analytics capabilities.

Design, build, and maintain scalable, secure data pipelines and platforms to acquire, transform, and store large financial datasets. Enable AI/GenAI-ready data foundations for model training, inference, retrieval, and RAG workflows. Collaborate with cross-functional teams, ensure data quality, governance, observability, and compliance, and support production deployments and troubleshooting.

The Senior Data Engineer will design and maintain scalable cloud-native data pipelines, develop APIs, implement advanced analytics, manage AI workflows, and enhance data operations collaboratively.

Design, build, and optimize scalable batch and streaming data pipelines. Model and process structured and unstructured data, ensure production reliability and observability, apply ML techniques for insight extraction, and drive data strategy while adopting modern tooling and IaC practices.

Build and own data platform infrastructure and scalable pipelines (ingestion, orchestration, transformation, delivery). Define governance, observability, and AI-first architecture. Optimize performance, ensure data quality, mentor engineers, and integrate AI-assisted tooling.

Design, build, and maintain scalable AWS-based data pipelines, data lakes and warehouses. Implement data modeling and warehousing for Redshift/Athena, ensure data quality, optimize queries, and develop production-grade Amazon QuickSight dashboards with security (RLS), performance tuning, and stakeholder-driven reporting.

Own and evolve the data platform: architect, build, and maintain data models, pipelines, and access layers. Ensure data quality, governance, and reliability; monitor and optimize infrastructure; provide technical leadership, code reviews, and collaborate with cross-functional teams to deliver scalable data solutions for operations and analytics.

The Senior Data Platform Engineer designs and maintains a scalable data platform in Snowflake, automates pipelines, and ensures data quality for analytical applications.

Lead technical design and hands-on development of Pantheon’s data platform, extending ingestion, curation, retention services and ML/LLM infrastructure. Mentor engineers, drive architecture and engineering standards, operate in a DevOps model, partner cross-functionally, and participate in on-call support to ensure reliable, compliant data access and analytics.