Top Senior Data Engineer Jobs in Denver & Boulder, CO

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.

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.

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.

Plans and executes enterprise data migrations, conversions, transformations, archival, and cloud data platform operations. Builds ETL/ELT pipelines and migration scripts, performs profiling and source-to-target mapping, validates data, manages cutovers and rollback procedures, and troubleshoots migration issues. Optimizes cloud platforms, databases, workloads, security, access, cost, and performance while collaborating with engineering, governance, architecture, and business teams.

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.

Design, build, and maintain scalable batch and streaming data pipelines using Databricks, PySpark, Python, SQL, Spark, and Delta Lake. Develop transformations, orchestration workflows, data models, validation processes, and curated data products. Troubleshoot performance and quality issues, apply governance and security practices, document solutions, and collaborate with architects, data scientists, DevOps teams, analysts, and clients.

Lead managed services for clients' Azure, Databricks, and Microsoft Fabric data environments. Architect, implement, monitor, troubleshoot, and govern data platforms, ETL pipelines, data warehouses, semantic models, reporting, and AI/ML solutions. Manage incidents and SLAs, advise clients on licensing and technical strategy, communicate solutions, document environments, and mentor distributed engineering teams.

Design, build, and maintain data lakes, data warehouses, and end-to-end data pipelines for a multi-tenant SaaS platform. Lead code and design reviews, write infrastructure-as-code, optimize performance and scalability, participate in data governance, mentor junior engineers, evaluate new technologies, automate tests, collaborate cross-functionally, and join on-call rotation to ensure reliable data delivery.

Designs, develops, and supports scalable GCP data pipelines, ETL/ELT workflows, database integrations, and data warehousing solutions. Uses BigQuery, Spanner, Dataflow, Informatica, and enterprise databases to enable analytics and applications. Responsibilities include performance tuning, monitoring GCP infrastructure, troubleshooting data quality and pipeline issues, scheduling jobs with Control-M, documenting technical procedures, and collaborating with engineers and architects in Agile environments.

Designs, develops, and optimizes enterprise data pipelines and platforms using Snowflake, integrating Oracle Fusion, on-premises databases, and cloud applications. Applies data quality, governance, security, and performance practices while supporting Power BI, AI, and machine learning use cases. Partners with business and technical stakeholders, leads data migrations, translates requirements into sustainable solutions, mentors junior engineers, and guides engineering standards.

Leads the design, development, and support of enterprise analytics, reporting, dashboards, and data models. Partners with business and technology teams to translate requirements into scalable solutions and actionable insights. Develops SQL transformations, Power BI and SSRS reports, and cloud analytics environments using AWS Athena, Snowflake, and Microsoft Fabric. Establishes data governance, quality, security, and reporting standards; troubleshoots complex issues; and provides technical leadership for modernization, cloud migration, mentoring, and continuous improvement initiatives.

Leads the design, development, and optimization of scalable data pipelines and analytics platforms. Builds ETL/ELT workflows using Databricks and Spark, integrates and transforms source data into curated models, manages SQL Server data, and maintains documentation and lineage. Collaborates with product, analytics, and engineering teams to deliver reliable data solutions. Applies responsible AI practices and delivers projects independently within established timelines.

Design, build, and optimize enterprise ETL/ELT pipelines, cloud data lakes, lakehouse solutions, data models, and semantic layers across AWS and Azure. Ensure data quality, security, performance, reliability, and governance while supporting analytics, reporting, and AI initiatives. Collaborate with product, engineering, IT, and business stakeholders to translate requirements into scalable data solutions, monitor pipeline health, troubleshoot issues, and improve data architecture and practices.