Top Data Engineer Jobs in Denver & Boulder, CO

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 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.

Leads enterprise data architecture and engineering modernization for USDA NRCS systems. Designs AWS cloud-native data platforms, migrates SQL Server and SSIS workloads, builds batch, streaming, and geospatial pipelines, and supports PostgreSQL/PostGIS databases. Responsibilities include database decomposition, DevSecOps automation, data governance, quality controls, disaster recovery, federal compliance, AWS migration, and AI/ML enablement using Bedrock and SageMaker. Provides technical leadership, architecture reviews, documentation, operational support, and knowledge transfer.

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.

Architects, builds, tests, and maintains scalable data infrastructure and ELT/ETL pipelines using AWS Glue, Glue Catalog, and Step Functions. Performs data modeling, SQL querying, data migration, schema optimization, validation, and data quality improvements. Partners with analytics and business teams to transform raw data into production-ready datasets. The role may also support AI/ML pipelines, governance, access controls, DevOps workflows, and real-time data architectures.

Own Eve’s data platform end to end, including ingestion, orchestration, incremental dbt models, Snowflake administration, Terraform infrastructure, observability, access controls, and pipeline reliability. Build schema-change detection, freshness SLAs, CI environments, and incident processes while managing performance and compute costs. Partner with analytics engineers and stakeholders to maintain trusted data for reporting and AI systems, using AI-assisted development and documenting platform standards.

Designs, builds, operates, and modernizes secure ETL/ELT data pipelines for public health interoperability. Responsibilities include using NiFi, Kafka, Python, and SQL; deploying pipelines on Azure or AWS; implementing CI/CD with GitHub Actions; monitoring with Grafana; troubleshooting production systems; supporting legacy migrations; and ensuring data security, compliance, scalability, and reliability. The role collaborates with architects, integration engineers, data scientists, DevOps/SRE, and product teams.

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

Leads data architecture and engineering modernization across business domains. Designs and implements complex pipelines, integrations, data models, and orchestration using Snowflake, AWS, and Airflow. Establishes data quality, governance, security, testing, observability, CI/CD, and reliability practices. Provides architectural guidance, evaluates technical solutions, leads cross-team initiatives, mentors engineers, and improves platform performance and cost efficiency.