Top Data Engineer Jobs in Denver & Boulder, CO

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.

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.

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 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.

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.

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.

Lead architecture and reliability of the data platform, build and harden ingestion (batch and CDC), own BigQuery+dbt transformations, establish testing/observability/governance, mentor engineers, develop Python tooling and APIs, run POCs for lakehouse/open table formats, and define AI-assisted development practices.

The role involves architecting and improving data systems, defining governance standards, optimizing performance, and mentoring engineers in data engineering.

Design, build, and deliver scalable data pipelines and products end-to-end. Partner with analytics, product, and engineering to ingest diverse data sources, implement dbt transformations, ensure data quality, CI/CD, observability, monitoring, and cost/performance optimization. Leverage AI/automation for self-service tooling and intelligent pipelines while driving stakeholder alignment and reliable production operations.

Lead design and optimization of high-throughput data ingestion pipelines and Python-based microservices. Improve pipeline performance, reliability, and observability across distributed systems, define unified ingestion architectures, work with Snowflake for analytics, mentor engineers, and collaborate cross-functionally to ensure scalable, fault-tolerant data platforms.

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).

Own SQL and dbt-based data modeling and data quality for clinical and claims datasets. Author and refactor dbt models/tests, triage DQ alerts, design monitors, optimize SQL performance, and collaborate with Data Engineering and stakeholders to ensure trusted production datasets.

Lead design and implementation of scalable cloud data lakes, warehouses, and marts. Build ELT/ETL pipelines, apply Data Vault 2.0 and dimensional modeling, implement data quality, lineage, observability, and governance. Optimize Snowflake/Databricks/Synapse platforms, champion CI/CD and DataOps, mentor engineers, and enable AI/ML-ready data products through collaboration with architecture and product teams.

Design, build, and operate scalable cloud data pipelines and platforms (Azure preferred). Implement DevSecOps, CI/CD, automated testing, monitoring, performance tuning, and production readiness. Mentor engineers, drive operational excellence, and translate requirements into robust technical designs. Apply healthcare domain knowledge and collaborate in agile teams to deliver secure, reliable data products.

Lead design and delivery of scalable, secure cloud data pipelines and products. Translate requirements into architecture, apply advanced data modeling (Data Vault 2.0/dimensional), embed data quality, observability, security, and DataOps/CI-CD practices, optimize performance and cost, mentor engineers, and enable AI/ML-ready datasets within an agile DevSecOps model.

Build and maintain data pipelines, applications, workflows, and governance within Palantir Foundry. Apply industrial engineering and operations research methods to analyze depot, MRO, and supply chain processes, integrate ERP/MRO data into models, run capacity/throughput analyses, deliver decision‑support briefings, and support adoption of advanced analytics and digital engineering. Coordinate with stakeholders and support business development activities.

Lead data engineering and advanced analytics work for DoD/IC clients: design and build AI/ML models (including LLMs/agentic AI), clean and retrieve data, visualize results for non-technical leaders, guide teammates, and operate on classified networks with required TS/SCI clearance and polygraph readiness.

Design and build data systems and analytics to turn large, structured and unstructured datasets into actionable insights. Develop and validate AI/ML models (including LLMs/agentic AI), clean and retrieve data, visualize results for non-technical leaders, and work on classified networks supporting DoW/IC clients.

Lead data engineering efforts to turn large, disparate datasets into actionable insights. Build and evaluate advanced analytics and AI/ML models (including LLMs/agentic AI), visualize results for non-technical leaders, work on classified networks, guide teammates, and advise clients. Requires conveying complex findings clearly and making go/no-go recommendations.

Support building and maintaining ETL/ELT data pipelines and cloud data workflows. Work with senior engineers and cross-functional teams to implement, test, deploy, and document data processes. Continuously learn cloud technologies, SQL, and Python/PySpark, follow coding best practices, and contribute to process improvements in an Agile environment.

Design, build, and maintain scalable data pipelines and ETL/ELT frameworks using Python and PySpark. Develop reusable components, microservices, and cloud-based data solutions on AWS and Snowflake. Ensure code quality via reviews, CI/CD, logging, and best practices while collaborating with stakeholders to solve data problems.

Support design, development, and maintenance of data pipelines and infrastructure. Assist with ETL/ELT workflows, data ingestion, cleaning, validation, modeling, and warehousing. Document processes and collaborate with data teams to support analytics use cases.

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.

Design and deliver Databricks lakehouse solutions: build ETL/ELT pipelines with PySpark and SQL, implement governance and Unity Catalog, integrate data platforms with applications via APIs/events, apply cloud architecture best practices, automate CI/CD for Databricks workloads, and consult with clients on requirements and solution design.