Top Senior Data Engineer Jobs in Denver & Boulder, CO

Build and operate large-scale audio data pipelines for AI model training. Responsibilities include discovering data sources, developing reliable data-processing systems, optimizing data quality, throughput, and cost, and collaborating with AI scientists. The role requires ownership of engineering projects from discovery through continuous improvement in a distributed, autonomous environment.

Build and operate large-scale audio data pipelines for AI model training. Responsibilities include discovering data sources, developing reliable data acquisition and processing systems, optimizing quality, throughput, and cost, and collaborating with AI scientists. The role requires ownership of engineering projects from discovery through continuous improvement, with emphasis on backend development, production systems, and scalable data processing.

Build and operate large-scale audio data pipelines for AI model training. Responsibilities include discovering data sources, processing petabyte-scale datasets, optimizing quality, throughput, and cost, collaborating with AI scientists, and owning engineering projects from discovery through continuous improvement.

Build and operate large-scale data pipelines that collect, process, and optimize audio datasets for AI model training. The role involves discovering data sources, improving data quality, throughput, and cost efficiency, collaborating with AI scientists, and owning engineering projects from discovery through continuous improvement.

Designs and maintains secure, reliable data platforms, including relational data models, migrations, pipelines, monitoring, data quality processes, and self-service reporting access. The role protects PHI and PII through access controls, encryption, audit logging, and governance in a HIPAA environment. It also involves backend API development, code reviews, security reviews, stakeholder collaboration, and mentoring teams on data practices.

Build customer-facing data products for the single-family rental market. Responsibilities include architecting real estate data models, designing reliable data pipelines and workflows, shaping data platform architecture, establishing engineering standards, and developing real estate domain expertise. The role partners with data science and product teams and requires leadership in scalable data engineering.

Own Moxie’s end-to-end data infrastructure, including pipelines, Snowflake warehousing, dbt and Omni modeling, orchestration, observability, and documentation. Partner with Engineering and Finance on schema changes, accounting logic, metrics, and business-critical reporting. Deliver financial or historical EMR data models, establish SOPs and reliability KPIs, optimize performance and cost, and help advance Practice Intelligence toward predictive and prescriptive products with appropriate AI safeguards.

Build and own Snout’s data foundation, including PostgreSQL and dbt warehouses, ingestion pipelines, dimensional models, data quality, documentation, and reliable rebuilds. Reconcile data from veterinary practice-management systems, support LLM/AI tooling, translate stakeholder needs into technical plans, and establish the foundation for a future data team and analyst.

Design, develop, and operate large-scale data ingestion, transformation, enrichment, and processing pipelines across cloud, hybrid, and on-premises environments. Build distributed services using Java, Python, and Kafka; optimize data models and platform performance; deploy applications on Kubernetes; support infrastructure automation and CI/CD; troubleshoot production systems; and collaborate with customers and solution architects on implementations.

Build and maintain production data pipelines, transformations, governed data products, and cloud data infrastructure for a federal background investigation platform. Develop Python and SQL workflows, contribute to Terraform and CI/CD, monitor data quality and reliability, support data science and ML teams, and operate within FedRAMP, NIST 800-171, and CUI requirements. Collaborate with engineering teams, troubleshoot platform issues, and document processes in a highly regulated environment.

Lead the design and delivery of scalable data solutions, including data pipelines, transformations, metadata frameworks, quality controls, and governance. Collaborate with stakeholders, product owners, and engineering teams to implement requirements, troubleshoot issues, test solutions, and improve engineering practices. Mentor junior engineers, lead proof-of-concepts, support CI/CD and automation, and ensure data architectures meet business and technical needs.

Leads and develops a data engineering team implementing Navigate360 integrations for partner campuses. Oversees integration design, data mapping, validation, reconciliation, quality controls, delivery planning, and scalable implementation processes. Partners with Product, Launch, and Partner Success teams to resolve technical and partner challenges, improve adoption, manage project health, and ensure reliable outcomes. Provides technical mentorship, code and design review, stakeholder communication, and continuous process improvement.

Design, build, and own business-critical data pipelines and infrastructure across warehouses, orchestration, BI, and multiple regions. Develop AWS Glue and Spark jobs, data models, event contracts, observability, and batch or streaming architectures. Collaborate across producer and consumer teams, support Federal workloads, and operate production data platforms in government cloud environments.

Lead the architecture and development of scalable AWS-based data ingestion, transformation, and orchestration pipelines. Build reliable data infrastructure using Python, SQL, Airflow, Lambda, ECS, SQS, and Terraform. Establish data modeling, quality, lineage, monitoring, testing, and observability practices while partnering with analysts, scientists, and backend engineers. Mentor senior engineers, guide technical decisions, and provide hands-on leadership for complex data platform initiatives.

Lead the architecture and development of Vercel’s next-generation data platform, supporting batch and real-time integrations, analytics, data warehousing, and AI/ML workloads. Design scalable systems using Kafka, ClickHouse, Tinybird, and Snowflake; establish data governance and security standards; guide architectural decisions and roadmaps; collaborate with engineering, product, security, compliance, and leadership teams; write production code; and mentor engineers.

Architect and lead enterprise data platforms, pipelines, and infrastructure using Spark, Databricks, and cloud technologies. Build scalable data models and dimensional schemas, establish governance and data quality frameworks, optimize performance and reliability, and implement monitoring. Partner with stakeholders to translate requirements into technical solutions, lead design reviews, mentor engineers, contribute to platform strategy, troubleshoot complex failures, and support hiring and team development.

Designs, builds, and maintains scalable ETL and data processing pipelines supporting analytics, forecasting, risk, and customer insights. Leads complex SQL development, data modeling, governance, quality, lineage, security, and compliance. Partners with engineering, finance, sales, compliance, product, analysts, and data scientists to translate requirements into reliable workflows. Evaluates emerging technologies, leads proof-of-concepts, and develops automated solutions using Python, Spark, Databricks, cloud platforms, orchestration tools, and modern data warehouses.

Leads the design, development, optimization, and migration of modern data platforms in Snowflake. Builds pipelines with dbt, Python, and cloud services; gathers client requirements; creates solution designs and project plans; advises on Snowflake performance, cost, security, and workload management; integrates BI, governance, and AI/ML tools; contributes to accelerators and mentors junior engineers. The role is remote, client-facing, consultative, and focused primarily on technical delivery rather than sales.

Build and operate batch and streaming data pipelines, ETL/ELT workflows, data warehouses, dimensional models, and reusable data services. Improve reliability, performance, quality, and cost efficiency through monitoring, testing, automation, and incident response. Partner with analytics, data science, product, business, platform, and infrastructure teams to deliver production data solutions and roadmap initiatives. Apply AI-assisted tools responsibly to development, testing, documentation, and troubleshooting.

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.

Build and maintain Databricks-based data platforms, including ingestion, transformation, storage, governance, data modeling, and serving pipelines. Establish medallion architecture standards, canonical data models, quality controls, lineage, schema evolution, and reliable batch or incremental processing. Improve pipeline observability, scalability, idempotency, and recoverability while moving curated data to systems such as ClickHouse. Collaborate across application and analytics teams to create durable, governed production datasets.

Build and operate scalable batch, streaming, and event-driven data pipelines, cloud data platforms, trusted data products, and analytical models. Improve data quality, observability, reliability, performance, and cost efficiency while supporting risk, finance, product, analytics, and AI initiatives. Partner cross-functionally, influence architecture, lead complex technical initiatives, mentor engineers, and establish strong engineering practices including testing, CI/CD, governance, and infrastructure automation.

Lead the architecture and evolution of enterprise-scale data platforms, applying distributed-systems and advanced data-modeling patterns. Establish technical standards across dbt, Airflow, Snowflake, Kafka, ETL, cloud infrastructure, security, monitoring, CI/CD, testing, and source control. Solve complex data problems, partner with senior leaders, provide technical governance, mentor engineers, and drive platform and process improvements.

Designs and advances Providence’s enterprise data platform through data architecture, governance, metadata management, Snowflake modernization, legacy migrations, and scalable integration patterns. Establishes engineering, DataOps, CI/CD, repository, and data quality standards while supporting analytics, reporting, AI, and data products. Collaborates across multidisciplinary teams to implement large-scale data initiatives in a healthcare environment.

Lead data engineering strategy and develop enterprise data lake solutions on AWS. Build data pipelines using AWS data integration, engineering, and analytics resources, develop API services with FastAPI or Flask, optimize engineering practices, create long-term data plans, and represent the team in executive meetings.