Top Data Engineer Jobs in Denver & Boulder, CO

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.

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 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.

Develop and maintain data pipelines using Python, SQL, ETL, and Apache Spark. Extract, transform, validate, and load data from databases, APIs, and files; monitor pipeline performance and quality; support batch and streaming workflows; document processes; and collaborate with analytics and business intelligence teams.

Develop and optimize data pipelines using Python and SQL; integrate data through REST APIs; support ETL/ELT workflows; perform data cleaning, validation, and quality checks; troubleshoot processing issues; use Git for collaborative development; document workflows; and collaborate with engineers and analysts in a remote environment.

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.

Build and maintain production ETL pipelines for structured and unstructured data, integrate machine learning models for enrichment, develop data transformations and quality monitoring, design warehouses and dimensional models, establish governance frameworks, and contribute to data architecture and platform improvements. Collaborate with data scientists, analysts, and engineering teams to deliver reliable, actionable intelligence.

Leads the architecture, development, and production operation of complex data pipelines and platforms. Resolves scalability, data quality, reliability, and architectural challenges; evaluates data technologies; mentors engineers; establishes engineering standards; partners with product, analytics, and operations teams; communicates technical trade-offs; and conducts proofs of concept to guide platform strategy.

Build and maintain scalable Snowflake-based ELT/ETL pipelines, production data models, and observability frameworks. Translate business requirements into reliable solutions supporting analytics, BI, and ML. Establish data quality, governance, lineage, security, and compliance practices while optimizing warehouse performance, cost, and data freshness. Collaborate with Analytics, Product, and Engineering teams, contribute to architecture decisions, evaluate Snowflake tooling, and use AI-assisted development tools to accelerate delivery.

Designs, builds, and optimizes scalable data collection, storage, access, and analytics systems. Creates and automates reliable data pipelines, supports data quality and governance, ensures SLA and performance compliance, and monitors pipeline operations. The role requires collaboration with analytics, business, and customer teams, with potential work involving AI/ML pipelines, Databricks, DevOps practices, streaming data, and event-driven architectures.