Top Data Engineer Jobs in Denver & Boulder, CO

Designs, develops, and supports analytics data solutions for clients, including Snowflake and dbt ingestion and transformation pipelines, data marts, validation, monitoring, governance, and documentation. Manages multiple consulting projects, budgets, timelines, quality assurance, troubleshooting, client communication, and implementation planning. Requires strong data engineering experience, regulated-industry knowledge, stakeholder communication, and willingness to travel.

Administer and enhance Edmunds’ Databricks data platform and AWS infrastructure. Build and maintain ETL pipelines, infrastructure-as-code tooling using Terraform or CDK, and operational dashboards, alerts, and reports. Collaborate with business, engineering, analytics, security, and infrastructure teams to support data platform users and AI solutions. Evaluate new technologies, troubleshoot platform issues, and improve operational, cost, and security visibility.

Supports the Mobile Device Data and Operations team by collecting, cleansing, enriching, and distributing product data to catalogs and vendor partners. Develops workflow designs and toolsets using KNIME or Alteryx, Oracle and Snowflake databases, and AWS. Participates in an 11-week full-time internship with professional development, networking, mentorship, and cross-functional activities.

Define and govern enterprise-scale data architecture across batch, streaming, warehouse, lakehouse, transactional, and AI use cases. Establish standards for data quality, lineage, access, cataloging, governance, observability, and SLAs. Architect AI-enabled workflows, resolve complex architecture issues, influence roadmaps, and mentor engineers through hands-on technical leadership. The role requires 15+ years of software, data engineering, or architecture experience and expertise in large-scale data platforms and modeling.

Build and maintain data pipelines, ingestion workflows, transformations, and data products using Python, SQL, and cloud data platforms. Test and troubleshoot systems while applying Git, software testing, and CI/CD practices. Collaborate with senior engineers and cross-functional stakeholders, use AI-assisted tools, and deliver reliable data solutions in a fast-paced startup environment.

Design, build, and lead scalable data platforms, pipelines, data products, and ELT/ETL processes supporting analytics and AI/ML workloads. Establish standards for data quality, testing, monitoring, observability, CI/CD, security, and reliability. Own projects through deployment and production support, collaborate across technical and business teams, use AI-assisted engineering tools, and mentor data engineers.

Lead the architecture and development of scalable AWS-based data ingestion, transformation, and orchestration pipelines. Build reliable data infrastructure using Python, SQL, Airflow, Lambda, ECS, SQS, and Terraform. Establish data modeling, quality, lineage, monitoring, testing, and observability practices while partnering with analysts, scientists, and backend engineers. Mentor senior engineers, guide technical decisions, and provide hands-on leadership for complex data platform initiatives.

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.

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.

Configure and maintain Adobe Experience Platform and Real-Time CDP data pipelines, XDM schemas, datasets, identity resolution, Profile enablement, segmentation readiness, and destination activation. Validate data quality, troubleshoot ingestion and identity issues, manage sandboxes, and implement privacy, consent, governance, and access controls. Partner with architects, data engineers, consultants, analysts, data scientists, and marketing teams to support reporting, personalization, and machine learning readiness.

Build scalable, secure data pipelines, models, platforms, and governance systems that enable AI applications to access trusted enterprise data. Partner with analytics, marketing, and product teams to translate requirements into technical solutions, independently deliver projects through production, and support sensitive-data security and access controls. Preferred experience includes LLM architectures, cloud data platforms, infrastructure as code, regulated environments, and technical leadership.

Design and scale lakehouse architecture, including Bronze, Silver, and Gold data layers. Build reliable streaming and batch pipelines with Kafka, Spark, and Airflow; manage Iceberg, Delta, and Hudi table formats; optimize Trino, Starburst, and Databricks queries; and improve platform reliability, data quality, and performance. Collaborate with data scientists, analysts, and product teams to deliver scalable data platform solutions.

Design and scale lakehouse architecture, streaming and batch pipelines, and reliable data platforms using Kafka, Spark, Airflow, Iceberg, Databricks, and related technologies. Build Medallion-layer data systems, manage open table formats, optimize distributed queries, monitor platform reliability, resolve data quality issues, and collaborate with analysts, data scientists, and product teams.

Build and maintain scalable ETL/ELT pipelines and data foundations using Databricks, dbt, and AWS. Transform raw structured and unstructured data into reliable datasets for product analytics and data science, including telemetry, streaming data, and feature engineering. Develop dashboards in Tableau and Databricks, optimize workflows, and collaborate with Product, Engineering, analytics, and data science stakeholders to support product health, adoption, and performance insights.

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 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).

Designs, develops, and supports SAP HANA reporting solutions and enterprise data products. Responsibilities include building data pipelines, calculation views, BI visualizations, embedded analytics, validation processes, metadata documentation, demos, and user training. The role partners with stakeholders, analysts, data scientists, and process teams to translate business requirements into deployable data solutions and improve the organization’s data platform.

Supports data engineering, ETL, database, platform, and integration workstreams. Responsibilities include data extraction, pipeline testing, environment setup, database support, source-system profiling, data cleansing, Ed-Fi ODS loading, validation, Oracle and SQL Server migration support, SharePoint configuration, year-end rollover tasks, and technical documentation under senior staff guidance.

Designs, develops, and maintains scalable cloud-based data solutions, ELT/ETL pipelines, data warehouses, curated datasets, and transformation logic using Snowflake and related tools. Optimizes SQL and warehouse performance, monitors workflows, supports data governance and quality initiatives, documents data flows, and delivers trusted datasets for Tableau reporting. Collaborates with business and technical stakeholders in Agile processes while ensuring secure handling of sensitive PHI data.

Build and configure a government Databricks workspace, develop ingestion pipelines from enterprise systems, and prepare data for reporting. Implement Unity Catalog governance, metadata, auditing, and lineage; configure MLflow workflows; assess security controls for sensitive data; and create architecture documentation, data dictionaries, and operating procedures. The role also requires integrating legacy systems, optimizing Delta Lake pipelines, and supporting long-term platform sustainment.

Performs data engineering operations including SQL scripting, ETL and data validation, query optimization, data analysis, dashboard maintenance, task-result validation, and troubleshooting with engineering and DBA teams. Ensures data integrity and compliance, manages ticket-based assignments, and supports data production and implementations. The role may require after-hours or weekend support and includes preferred experience with network security platforms, certificate management, and automation scripting.

Designs and maintains ELT pipelines, warehouse integrations, data models, and data quality processes. Collaborates with data scientists, analysts, engineers, and stakeholders to deliver scalable analytical solutions. Responsibilities include data integration, governance, security, performance optimization, exploratory analysis, visualization, and cloud-based API ingestion. Requires strong SQL, DBT, Snowflake, Azure Data Factory, cloud, and scripting experience, plus leadership and communication skills.

Design, build, and maintain scalable cloud data lake infrastructure supporting analytics, reporting, and AI/ML. Develop batch and real-time ETL/ELT pipelines, data quality frameworks, governance policies, metadata and lineage systems, security controls, monitoring, disaster recovery, and performance optimization. Collaborate with IT, analytics, operations, and AI teams to ensure data integrity, compliance, accessibility, and effective use of enterprise datasets.

Build and operate data-intensive web products and APIs, extend high-throughput ETL pipelines and scrapers, ensure reliability/observability, design self-scaling services, participate in code review, and collaborate across product, data, and infrastructure teams to meet production SLAs.