Top Data Engineer Jobs in Denver & Boulder, CO

The Data Engineer will build and maintain data pipelines, optimize workflows, manage infrastructure, and enhance pipeline observability while collaborating with teams for effective data delivery.

Design and implement data architecture and scalable ETL/ELT pipelines using cloud platforms (AWS/Azure/GCP). Develop data models, ensure governance and security compliance, analyze complex problems, mentor junior staff, build client relationships, and enhance technical skills to deliver high-quality data solutions.

Design and build data architecture and cloud-based data infrastructure; develop ETL/ELT pipelines and data integration solutions; deploy scalable storage on AWS/Azure/GCP; analyze complex issues, mentor junior staff, ensure data governance/security compliance, and build client relationships.

Design and implement scalable data solutions and advanced data ecosystems. Build ETL/ELT pipelines, data warehousing, and governance. Analyze complex problems, mentor junior staff, maintain high-quality deliverables, and build client relationships to align solutions with business context.

Design and build data infrastructure, pipelines, and integration solutions using cloud and big-data tools. Develop and manage data lakes and warehouses, apply data architecture and dimensional modeling, validate data quality, implement security best practices, and collaborate with clients to deliver analytics and data strategies. Mentor peers and manage growing client responsibilities as a Senior Associate.

Design, build, and maintain data infrastructure, pipelines, and integrations using cloud and big-data technologies (AWS, Azure Data Factory, Airflow, Hadoop, Databricks). Ensure data quality, security, and scalable storage (data lakes/warehouses); collaborate with clients to deliver analytics and data-driven strategies.

Lead design and implementation of data architecture, models, ETL/ELT pipelines, and cloud data warehouses/lakes. Ensure data governance, security, and pipeline reliability while mentoring and managing a team and collaborating with stakeholders to translate business requirements into technical solutions.

Lead design and delivery of cloud-based data architecture, data models, ETL/ELT pipelines, data warehouses/lakes, and data security. Manage projects, mentor team members, ensure governance compliance, collaborate with stakeholders, and optimize cloud infrastructure for performance, cost, and scalability.

Design, build, and maintain ETL pipelines and data architectures. Create reports and dashboards with BI tools, optimize SQL, support data quality improvements, and collaborate with clients on analytics-driven business insights in regulated environments.

Lead design and implementation of data architecture, build data pipelines and models, ensure governance and security compliance, manage large-scale projects, advise clients strategically, and coach high-performing teams to deliver data solutions.

Lead design and implementation of data architecture and cloud-based data engineering solutions. Manage large projects, translate stakeholder requirements into technical designs, ensure data governance and security, develop data models and pipelines, advise clients strategically, and coach teams to deliver operational excellence.

The Data Engineer will develop and implement ETL processes, design data architectures, work with data platforms, and ensure data solutions meet scalability and security needs. Responsibilities include mentoring junior engineers and collaborating with stakeholders to gather requirements and optimize data transformations.

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.

As a Data Engineer, you'll lead data engineering projects, develop data transformations, build workflows with Apache Airflow, and ensure data quality and integrity while collaborating with stakeholders.

As a Senior Data Engineer at Jellyfish, you'll build and maintain data pipelines, optimize orchestration, automate CI/CD processes, and enhance data integration while ensuring high performance and reliability.

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.

Lead architecture and buildout of a new graph-backed enterprise data platform: design ingestion, graph and relational storage, entity resolution pipelines, temporal models, ETL/ELT pipelines, governance, APIs, and production connectors. Ship scalable graph data models, traversal queries, and platform roadmap while enabling observability, security, and containerized deployments.

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 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.

Lead data engineering engagements within managed services: design data architectures, build and maintain ETL pipelines, implement BI solutions, coach teams, engage stakeholders, and validate outcomes to optimize client performance.

Lead data engineering projects to design and maintain ETL/ELT pipelines and architectures, leverage BI and cloud analytics (AWS/Redshift, S3, Glue), implement DataStage and CDC solutions, build reporting and visualizations (QlikView, Spotfire), manage and mentor teams, collaborate with stakeholders, ensure project delivery, performance tuning, and promote new technologies and process improvements.

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.

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

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.