Top Data Engineer Jobs in Denver & Boulder, CO

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.

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 support enterprise data ingestion, transformation, modeling, and integration solutions using Microsoft Fabric and Azure data services. Develop batch, API, file, database, and event-driven pipelines; create dimensional and curated data models; write and optimize SQL; resolve data-quality and pipeline issues; and support production workloads. Collaborate in Agile engineering processes using GitHub, Azure DevOps, CI/CD, code reviews, and reusable development standards.

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.

Designs, develops, deploys, and supports complex enterprise data pipelines, data models, ETL/ELT processes, and analytical solutions. Leads data governance engineering initiatives involving metadata catalogs, lineage, data quality, and remediation. Builds governed, AI-ready datasets and integrates AI capabilities into production data workflows while validating performance, security, privacy, and reliability. Collaborates with business, technology, data science, and application teams to deliver scalable, compliant solutions.

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.

Design and maintain scalable AWS-based data pipelines, architectures, ETL/ELT workflows, databases, and big data solutions. Integrate and transform complex datasets using Python, SQL, Spark, Hadoop, and Kafka. Build data models, dashboards, reports, and analytics solutions while implementing data quality, governance, security, monitoring, and compliance processes. Support migrations and modernization projects, collaborating with data scientists, analysts, and business stakeholders to deliver reliable, decision-ready data.

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.

Build and operate Databricks-based batch and streaming pipelines for analytics and enterprise AI workloads. Design lakehouse data models, governance, access controls, quality monitoring, and reliable cloud infrastructure. Develop ingestion and chunking pipelines for unstructured documents, embeddings, vector search, and LLM evaluation datasets supporting RAG systems. Collaborate with AI and platform engineers while applying software engineering practices such as version control, testing, and CI/CD.

Maintains and monitors Azure Databricks data pipelines, troubleshoots data issues, collaborates with stakeholders on data requirements, documents processes, performs backups and disaster recovery drills, and supports data access and usage. The role requires extensive data engineering experience, Azure expertise, incident management knowledge, and strong problem-solving skills.

Build and maintain reliable data pipelines, computed tables, and scalable data models using SparkSQL and PySpark. Integrate IoT, product, customer, external, unstructured, and ML-related data into a central data lake for analytics, causal inference, model training, and dashboards. Ensure high data quality and uptime while collaborating with data science, analytics, AI/ML, and engineering teams.

Manage and analyze mine-site operational data, consolidating planning, production, maintenance, and performance information into a centralized Technical Data Center. Deploy and support Python, Lua, SQL, and on-premises applications; monitor servers and infrastructure; troubleshoot operational applications; and collaborate with technical, operating, innovation, and business improvement teams on data integration and innovation initiatives.

Lead the reverse-engineering and migration of legacy ETL pipelines from SAP BusinessObjects Data Services and JD Edwards into dbt and Databricks architectures. Build automated data validation, maintain SQL Server write-back mechanisms, collaborate with client stakeholders, and lead and mentor a small engineering team. The role requires hands-on expertise in SQL, Python, Spark, orchestration, cloud platforms, and legacy ERP and ETL migrations.

Designs, builds, tests, and maintains scalable data infrastructure and ETL/ELT pipelines for mission partners. Uses AWS Glue, Glue Catalog, and Step Functions to integrate, migrate, transform, and validate data. Develops data models, schemas, queries, and production-ready datasets while collaborating with analytics and business teams. The role may also support AI/ML pipelines, data governance, DevOps workflows, Databricks, and real-time streaming architectures.