Top Data Engineer Jobs in Denver & Boulder, CO

Supports the Mobile Device Data and Operations team by collecting, enriching, and distributing product data to catalogs and vendor partners. Develops workflow designs using KNIME or Alteryx, works with Oracle and Snowflake databases, and deploys application tools on AWS. The internship also includes professional development, mentorship, networking, community service, and cross-functional projects.

Design and develop data pipelines, integrations, transformations, and infrastructure across AWS, Snowflake, Airflow, and MySQL. Troubleshoot warehouse issues, validate data quality, improve pipeline reliability, and translate business needs into data engineering solutions. Interpret technical findings for client recommendations, manage workstreams, review junior team members’ work, and provide technical guidance across managed services engagements.

Leads enterprise-scale data architecture across batch, streaming, warehouse, lakehouse, transactional, AI, and vector-store use cases. Establishes data quality, governance, lineage, observability, access-control, and reliability standards. Defines AI-enabled data engineering patterns, mentors engineers, resolves complex architecture issues, and influences cross-functional roadmaps. Requires hands-on technical leadership and 15+ years of software, data engineering, or architecture experience.

Leads the design, development, implementation, and support of scalable data solutions across multiple applications and platforms. Responsibilities include data requirements analysis, ETL/ELT development, data modeling, migration and transformation, workflow orchestration, performance optimization, data quality, pipeline monitoring, and production support. The role also provides technical leadership through coding standards, code reviews, mentorship, and engineering best practices.

Build and maintain Terraform-managed Databricks data platforms on AWS, develop secure external data-sharing capabilities, implement data governance controls, automate audit evidence collection, and improve platform reliability, performance, monitoring, and cost efficiency. The role also involves incident resolution, on-call participation, data pipeline development, compliance collaboration, and designing secure-by-default infrastructure for publisher partners.

Architects, deploys, and operates scalable data platform infrastructure supporting analytics. Responsibilities include administering Airflow and Superset, managing Terraform infrastructure, building CI/CD pipelines, implementing security and compliance controls, developing dbt data models, cataloging assets in OpenMetadata, automating with Python, and mentoring engineers. The role requires extensive infrastructure and data engineering experience, cloud expertise, container orchestration, and eligibility for CJIS clearance.

Build and maintain production data pipelines, dbt models, Airflow workflows, and governed datasets supporting People Analytics. Integrate Workday and other HR systems into reliable Snowflake data products, implement data quality monitoring, row-level security, semantic models, and AI agents. Partner with engineers, analysts, data scientists, and business stakeholders to deliver trusted data solutions, communicate technical trade-offs, enforce governance, and mentor other engineers.

Leads a small data engineering team while contributing hands-on development. Responsibilities include designing and maintaining dbt and Python ETL pipelines, managing data quality and UAT, coordinating implementation timelines, conducting code reviews, mentoring engineers, documenting systems, and supporting project delivery. The role also involves building and deploying AI agents for data quality, pipeline monitoring, and analytics workflows, with a focus on healthcare claims data and large-scale datasets.

Develop and operate cloud-based data pipelines, dimensional data models, data warehouse solutions, CI/CD automation, and platform governance controls. The role integrates APIs and diverse data sources while supporting security, monitoring, incident response, and reliable data product delivery. Responsibilities include collaborating with product owners and engineering teams, participating in design and code reviews, troubleshooting technical issues, and applying repeatable DevOps practices.

Leads the architecture, administration, engineering, and strategic evolution of enterprise Hadoop-based data platforms. Owns cluster lifecycle, scalability, reliability, security, disaster recovery, monitoring, optimization, upgrades, and operational standards. Troubleshoots distributed systems, supports data ingestion and governance, guides hybrid cloud strategies, documents platform operations, and mentors engineering teams. The role also develops data views, exports, and automated processing while collaborating with data engineering, analytics, security, and infrastructure teams.

Build and own AAG’s consolidated data warehouse, including production-grade infrastructure, ELT pipelines, dimensional models, and reporting layers. Integrate data from ERP, CRM, and manufacturing systems into Databricks on Azure, maintain reliable data flows, and support business decision-making across the organization. Collaborate with leadership to translate requirements into code, while managing system development, maintenance, documentation, and continuous improvement.

Leads geospatial data engineering across end-to-end pipelines, from ingestion and GDAL/OGR transformations through analyst-facing visualization. Develops Python and Jupyter-based analytical environments, manages GeoParquet data lake pipelines, administers ArcGIS services, and integrates FOSS4G tools including QGIS, GeoServer, and MapServer. Collaborates on spatial indexing and AI-assisted retrieval, leads technical refinement sessions, and supports PI Planning for data engineering initiatives.

Build and maintain analytical datasets, dimensional models, Power BI semantic models, and governed data transformations in Microsoft Fabric. Translate business requirements into reliable data products, perform testing and reconciliation, troubleshoot refreshes and deployment issues, and support Git-based development and CI/CD practices. The role requires independent ownership of low- to moderate-complexity engineering work, technical documentation, and collaboration with senior engineers, DataOps, governance teams, consultants, and business stakeholders.

Develops and supports cloud and on-premises data infrastructure, scalable batch and real-time data pipelines, data APIs, and delivery services. Collaborates with engineers, data scientists, analytics teams, and business owners in an agile environment. Implements enterprise data management practices covering master data, metadata, quality, lineage, and integration. Requires experience with data engineering, distributed applications, Linux, SQL, DevOps, and CI/CD, with AWS and Snowflake experience preferred.

Build and operate cloud data capabilities, including ingestion pipelines, transformations, data models, curated data products, and data-quality controls. Use Snowflake, dbt, SQL, and Python to deliver governed data for analytics, applications, automation, and AI workflows. Implement testing, CI/CD, documentation, lineage, access controls, monitoring, incident resolution, and performance optimization. Collaborate with engineering, analytics, governance, security, and business teams in an Agile, product-oriented environment with active AI-assisted development.

Designs and delivers large-scale, governed data pipelines and infrastructure across the full data lifecycle. Responsibilities include coding in Python and SQL, optimizing Airflow, dbt, AWS, Kafka, and Iceberg/Parquet workflows, implementing data quality and observability, contributing to architecture and tool evaluations, mentoring engineers, collaborating with stakeholders, and participating in on-call support.

Develop and deploy data pipelines and platforms that organize structured and unstructured data from disparate sources. Perform data manipulation, ETL, API-based querying, parsing, and processing. Build visualizations, dashboards, and lightweight web applications to communicate insights. Collaborate with analysts, developers, data engineers, and data consumers in agile teams while supporting scalable platform design, development, maintenance, and technical problem-solving for mission-driven clients.

Own Tableau Cloud data pipelines, published data sources, semantic models, Tableau Prep flows, data quality checks, governance, security, and performance optimization. Build reliable integrations from Salesforce, PostgreSQL, AWS, and NetSuite; validate reporting against approved figures; prepare models for Tableau MCP and AI agents; document lineage and train Tableau authors.

Build and govern Azure and Databricks data platforms, production pipelines, curated tables, data standards, quality controls, and audit-ready workflows. Manage operational and telemetry data, support Tableau reporting and AI/LLM use cases, and co-own Databricks workspace change control. Collaborate with data science, IT, operations, engineering, compliance, vendors, and business analysts to deliver reliable, documented, analysis-ready data.

Design and modify data solutions using Databricks and Palantir Foundry; build data models and reports from ERP and legacy-system datasets; define and execute tests; and translate stakeholder reporting needs into technical specifications. The role requires SQL, Python, PySpark, database architecture, cloud architecture, software engineering practices, independent execution, documentation, and collaboration. It supports an Army client in a hybrid Arlington, Virginia environment and requires an active Secret clearance.

Build and maintain scalable data pipelines and platforms that organize structured and unstructured data. Develop software for retrieving, parsing, and processing data; use Python, ETL procedures, and APIs; and create visualizations, dashboards, or lightweight web applications. Collaborate with analysts, engineers, developers, and data consumers in an agile environment to deliver solutions supporting mission-driven client projects.

Designs and delivers scalable, full-stack data solutions across collection, ingestion, processing, storage, access, and delivery. Builds ETL/ELT pipelines, data models, reporting and analytics systems, and secure data architectures. Supports AI and machine-learning systems, optimizes performance, resolves production issues, establishes data standards, and integrates enterprise and graph technologies. Requires extensive data engineering, architecture, analysis, clearance, and specialized platform experience.

Build and maintain full-stack data solutions across the data lifecycle, including ingestion, transformation, modeling, storage, security, analytics, and delivery. Develop SAP HANA solutions, ETL pipelines, data models, automation, and data warehouse capabilities across relational, NoSQL, on-premises, and cloud environments. Improve system performance, establish data standards, and collaborate on enterprise data architecture and business transformation initiatives.

Designs and delivers full-stack data solutions across ingestion, processing, modeling, storage, security, and analytics. Builds automated data pipelines, develops SAP HANA calculation views, optimizes performance, integrates structured and unstructured data, and supports data warehouse and data fabric initiatives. Maintains data systems, defines standards, and collaborates on enterprise architecture and process improvement. The role is virtual and requires U.S. citizenship.

Designs and maintains enterprise data pipelines, warehouses, feature stores, and AI/ML-ready datasets. Responsibilities include data modeling, ETL development, ontology design, data quality, security controls, performance optimization, and full lifecycle deployment. The role partners with data scientists and AI/ML teams in classified facilities, requiring Secret clearance and adherence to secure program operations.