Top Senior Data Engineer Jobs in Denver & Boulder, CO

Design, build, and operate scalable batch and streaming data pipelines, lakehouse and warehouse models, and data services. Own datasets end to end, improve Snowflake, Iceberg, Spark, and Flink performance and reliability, implement governance and observability, and support graph-serving data models. Partner with product and engineering teams, participate in on-call, review code and designs, and mentor junior engineers.

Design, build, deploy, and optimize enterprise Lakehouse data pipelines and ETL processes using Databricks, AWS, Python, and PySpark. Responsibilities include data modeling, Delta Lake and Unity Catalog implementation, performance tuning, pipeline troubleshooting, data security and compliance, legacy SQL analysis, and translating raw data into actionable insights. The role also involves cross-functional collaboration and may require eligibility for a Public Trust or Suitability/Fitness determination.

Own data pipelines into and out of the warehouse, dbt models, data integrity, governance, identity resolution, reverse ETL, and quality monitoring across growth, lifecycle, and brokerage data. Partner with product, marketing, and operations teams to establish data contracts, ensure accurate and timely business metrics, and support regulated financial data requirements.

Architects, deploys, and operates scalable data platform infrastructure supporting analytics. Responsibilities include administering Airflow and Superset, managing Terraform infrastructure, building CI/CD pipelines, enforcing security and compliance, developing DBT data models, cataloging assets in OpenMetadata, writing Python automation, and mentoring engineers. The role requires extensive infrastructure and data engineering experience across cloud platforms, containers, data security, and workflow orchestration.

Design and build scalable data infrastructure, including lakehouse architecture, ClickHouse deployments, ETL/ELT pipelines, and data models connecting Postgres with analytics systems. Establish data quality, observability, performance, cost, and reliability standards. Collaborate with product and engineering teams, define data contracts and specifications, support real-time analytics, and use AI-assisted coding and LLMs to accelerate pipeline development and transformation.

Build and optimize enterprise data warehouses, data lakes, and scalable batch and streaming pipelines. Support Snowflake and Databricks ecosystems, resolve ETL/ELT bottlenecks, automate workflows with Airflow and Control-M, and manage AWS-based infrastructure. Develop production-grade Python, SQL, and shell solutions while applying DevOps, CI/CD, IaC, and data modeling practices. Lead incident management, root cause analysis, operational metrics, and Agile collaboration with data and platform teams.

Lead the design and implementation of a scalable contract data model, including migration strategies, data architecture, pipelines, warehouses, analytics layers, governance, security, and quality monitoring. Partner with Product, Engineering, Data Science, ML, Professional Services, and customer-facing teams. Drive architecture decisions, provide technical mentorship, lead reviews, and manage multi-quarter data initiatives for a SaaS platform.

Build and maintain connectors for Oracle B2C Service, SharePoint, OneDrive, Teams, and Sitecore. Develop an AWS ingestion pipeline that extracts, transforms, deduplicates, enriches, permission-tags, indexes, and retires content in S3 and Amazon Bedrock Knowledge Bases. Implement synchronization, throttling, retries, failure isolation, monitoring, inventory reporting, documentation, and client transition support.

Lead customer-facing data engineering engagements from architecture through production handover. Build and optimize batch and streaming Lakehouse pipelines on Databricks using PySpark, SQL, and Delta Lake. Establish Medallion architecture conventions, implement data quality and automated testing, optimize Spark performance and platform costs, and mentor engineers. Partner with architects and explain technical trade-offs to customers.

The AVP, Data Engineer will design and build data ingestion, transformation, and replication pipelines using SQL, Python, dbt, and Dagster. Responsibilities include translating requirements into technical designs, developing data products, debugging production issues, supporting testing, and improving data engineering practices. The role also involves data modeling, mentoring junior engineers, conducting code reviews, and providing technical leadership while remaining hands-on. Experience with modern data platforms, workflow orchestration, cloud ecosystems, and insurance or financial services is preferred.

Develop enterprise data warehouse solutions and ETL pipelines using Azure Data Factory, Databricks, Snowflake, and related technologies. Process structured and unstructured healthcare data, including HL7 and FHIR files. Build stored procedures, perform data warehouse modeling, prepare ETL specifications, and use Python, source control, and scripting tools to support data integration and engineering initiatives.

Design, develop, test, deploy, and monitor scalable data pipelines using Azure Data Factory, Databricks, Python, SQL, and dbt. Integrate source systems with data warehouses and other platforms, automate migration processes, improve data delivery and infrastructure scalability, and lead data migrations for mergers and acquisitions while maintaining data security, retention, and privacy best practices.

Designs, builds, and operates scalable batch and streaming data pipelines, ETL/ELT workflows, cloud solutions, and secure Python REST APIs. Develops data foundations for machine learning, generative AI, embeddings, and retrieval-based applications. Ensures data quality, security, observability, lineage, and reliability while optimizing performance and cost. Leads architecture and code reviews, establishes engineering standards, mentors engineers, and collaborates with product, analytics, data science, and machine learning teams.

Develops, architects, and maintains production ETL/ELT and C#/.NET data pipelines across cloud data platforms. Responsibilities include supporting Iceberg, DynamoDB, AWS Lambda, and Trino/Starburst systems; onboarding data sources; tuning performance; monitoring data quality; resolving incidents; coordinating schemas; documenting processes; mentoring engineers; and supporting releases, deployments, architecture, and roadmap decisions.

Build and maintain SQL-based integrations connecting behavioral healthcare EHR/EMR systems to the Eleos platform. Investigate pipeline failures, data discrepancies, and quality issues to identify root causes. Develop reusable extraction, automation, and validation tooling; document data models, mappings, and business logic; and create standardized data models. Collaborate with platform engineers, product teams, and customer technical teams to establish secure access and deliver scalable, reliable integrations.

Design and operate Alpaca’s scalable data platform on GCP, including lakehouse infrastructure, Kubernetes-based deployments, streaming and CDC pipelines, batch ingestion, BI access, cataloging, monitoring, and governance. Build infrastructure as code with Terraform and Ansible, operate distributed query engines and Apache Iceberg, maintain reliability practices, and collaborate with DevOps and analytics teams to support rapidly evolving data requirements.

Own and improve Life360’s Ads Data Engineering platform on Databricks. Build, operate, and optimize batch and streaming pipelines, Delta Lake tables, dbt models, semantic layers, and core metrics. Integrate advertising and business data, implement quality and governance controls, manage AWS infrastructure with Terraform, reduce compute costs, and mentor engineers. Partner with analysts, ad strategists, and sales teams to deliver trusted data products while developing AI-assisted engineering tools and workflows.

Build and maintain production data ingestion, transformation, and pipeline code for a governed federal data and AI platform. Develop data products, support data science and ML teams, monitor quality and reliability, troubleshoot issues, and contribute to Terraform infrastructure and CI/CD workflows. Apply data governance, cataloging, access controls, and compliance practices within FedRAMP Moderate, NIST 800-171, and CUI environments. Collaborate with engineers and document pipelines and processes.

Own and operate NVIDIA’s production data platform, including pipelines, schemas, and data services supporting DGX Cloud reliability. Ensure uptime, freshness, quality, observability, and incident response while defining operational metrics and continuous improvements. Translate customer data needs into platform capabilities, establish engineering standards, review designs and code, mentor engineers, and drive the platform roadmap. The role requires strong Python, Databricks, Delta Lake, medallion architecture, data modeling, lineage, retention, and access-policy expertise.

Lead the technical direction for audience data products and build high-volume pipelines for audience creation, partner data onboarding, identity mapping, and external data delivery. Mentor engineers, oversee design reviews and build-versus-buy decisions, and develop AI-enabled engineering tooling with appropriate guardrails. Collaborate with data science, product, engineering, and external partners.

Lead the technical direction for identity and audience data systems. Design identity models, partner mappings, high-volume production pipelines, privacy controls, data quality frameworks, monitoring, and anomaly detection. Collaborate with data science, product, legal, security, and engineering teams while mentoring engineers and leading design reviews. Develop AI-enabled engineering tools and guardrails, and ensure reliable, auditable data products at scale.

Lead the architecture and evolution of enterprise-scale data platforms. Establish standards for dbt, Airflow, Snowflake, Kafka, ETL, security, monitoring, CI/CD, data modeling, and software development practices. Solve complex distributed-systems and data challenges, partner with senior leaders, mentor engineers, and drive platform improvements. The role requires strong data engineering expertise across cloud services, infrastructure tooling, data warehouses, orchestration, streaming, and programming.

Own and operate the Snowflake warehouse and Sigma semantic layer; manage ingestion (Fivetran), dbt model scoping and review, reverse-ETL, data quality SLAs, dashboards, month-end financial analytics, vendor management, and automation to support AdOps, Sales, Finance, and executive reporting.

Design and maintain Snowflake-based data platforms, cloud-native data lakes, and scalable ETL/ELT pipelines for asset-backed finance analytics. Orchestrate workflows with Dagster and AWS services, ensure data reconciliation and integrity, and support migration from legacy systems. Contribute to project planning, Jira backlog management, testing, cutover planning, and cross-functional go-live readiness.

Design and maintain Snowflake data pipelines and data lake architectures for asset-based finance analytics. Develop ETL/ELT workflows, validate and reconcile financial data, migrate legacy systems, and translate quantitative finance logic into scalable architectures. Support project execution through requirements breakdown, planning, testing, cutover, and go-live readiness. Collaborate across engineering, quantitative analytics, and stakeholder teams.