Top Senior Data Engineer Jobs in Denver & Boulder, CO

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.

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.

Build and own production data infrastructure for a healthcare AI platform. Responsibilities include REST API ingestion, Azure Event Hubs streaming into Databricks, Delta Lake medallion pipelines, entity resolution, semantic metrics, data-quality instrumentation, reconciliation, schema-drift handling, and troubleshooting source-to-warehouse discrepancies. The role requires strong Python and SQL skills, independent pipeline ownership, and experience with Databricks, Spark, PySpark, streaming, and healthcare-related data is a plus.

Designs trusted Snowflake data products and enterprise semantic models that standardize business concepts, metrics, relationships, and rules. Builds Cortex semantic views and agents, validates AI-generated results, develops regression tests, and maintains metadata, lineage, and documentation. Leads modeling initiatives from discovery through production while partnering with business and technical stakeholders to improve analytics, automation, and AI consistency.

Build and guide data platform capabilities supporting marketing, finance, analytics, and data science. Develop data collection, integration, scoring, experimentation, analytics, and infrastructure solutions; contribute code, lead cross-functional technical initiatives, improve system resiliency through incident response, and establish engineering standards. Partner with business and technical stakeholders to define strategy and long-term roadmaps.

Lead financial data engineering initiatives involving commissions, incentive programs, and backend financial systems. Build scalable, accurate, and reliable data pipelines, platform tooling, partner integrations, analytics infrastructure, and data science infrastructure. Shape technical strategy and roadmaps, collaborate with Finance, Product, Data Science, and Engineering, mentor engineers, contribute code, lead incident response, and establish engineering standards.

Own and evolve UJET’s data platform, including MySQL CDC replication to BigQuery, dbt transformations, Terraform infrastructure, pipelines, data quality, observability, and Looker models. Build reliable, analytics-ready datasets and trusted metrics for internal and customer-facing reporting. Partner with Analytics, Finance, Product, and Engineering to deliver scalable data solutions, improve platform reliability, optimize performance and costs, and establish development standards and guardrails.

Designs, builds, tests, and maintains secure, scalable cloud data platforms, pipelines, ETL/ELT processes, data models, data marts, integrations, and reporting services. Supports data migration, archiving, disaster recovery, governance, metadata management, data quality, CI/CD deployments, and performance optimization. Collaborates with architecture, governance, migration, QA, and business teams in an Agile federal environment while maintaining audit-ready documentation and mentoring junior engineers.

Designs, develops, and maintains scalable cloud-based data pipelines and enterprise data solutions. The role covers data ingestion, transformation, orchestration, integration, data lakes, cloud warehouses, batch and streaming processes, performance optimization, governance, and lineage. The Senior Data Engineer collaborates with analysts, data scientists, engineers, stakeholders, contractors, and vendors while mentoring junior staff. Core tools include Databricks, dbt, Azure Data Factory, SQL, Python, and Azure.

Design, build, and scale enterprise data products, pipelines, and platform capabilities using Databricks, Spark, Python, SQL, and cloud technologies. Modernize analytical solutions into governed production systems while implementing data quality, observability, lineage, security, monitoring, and governance. Partner with product, analytics, architecture, and business teams on technical design and complex data initiatives. Optimize platform performance and reliability, troubleshoot issues, establish engineering standards, and mentor other engineers.

Own and evolve WorkOS’s internal data platform, including ingestion, orchestration, Snowflake, dbt models, data governance, reverse ETL, semantic views, and AI-enabled data workflows. Ensure pipeline reliability, freshness, security, and scalability while partnering with Product, Finance, RevOps, GTM, Engineering, and Security. Build monitoring, runbooks, CI/CD gates, access controls, masking policies, and infrastructure automation across AWS and Kubernetes.

Build and scale secure data lakehouse and warehouse infrastructure supporting analytics, machine learning, clinical AI performance monitoring, and self-service insights. Develop scalable data pipelines, models, orchestration, quality controls, and LLM-enabled analytics tools using heterogeneous clinical and EHR data. Collaborate with engineering, data science, clinical, product, and client success teams to establish trustworthy metrics and technical solutions.

Own end-to-end client data migrations: reverse-engineer legacy ETL, design and build Airflow DAGs and Spark jobs, validate and reconcile data at scale, document architecture and decisions, and coordinate cross-functional teams through go-live and hypercare.

Design, build, and operate production data pipelines using Python, Spark SQL, and Airflow. Canonicalize healthcare and customer data into trusted internal models, optimize distributed processing, enforce schemas, and implement quality checks and observability. Support customer onboarding, streaming pipelines, troubleshooting, on-call operations, and platform best practices while collaborating with product, analytics, engineering, data science, and customer teams.

Own the analytics data layer by translating stakeholder needs into certified dimensional models, governed metrics, semantic layers, dashboards, and reports. Build with dbt, SQL, Databricks, Power BI, and Airflow while ensuring data quality, observability, reconciliation, documentation, and dependable orchestration. Partner closely with finance, operations, and client-facing teams, automate recurring workflows, and use AI-assisted engineering practices in a fully remote environment.

Own the technical design and operation of Palmetto’s governed data platform. Build trusted data products, semantic models, ingestion and transformation pipelines, secure access interfaces, and agent-ready data services. Establish governance, masking, authorization, observability, CI/CD, and audit readiness. Partner with finance, operations, product, and engineering teams while mentoring engineers and setting cross-team technical direction.

Designs, builds, and maintains Databricks data pipelines and analytics solutions using SQL and Python. Collaborates with development and IT teams, applies DevOps and CI/CD practices, supports Power BI dashboards, and contributes to data modeling, management, and governance. The role requires extensive Databricks and cloud-platform experience, strong communication skills, fluent English, and at least five years of relevant experience.

Build scalable data ingestion, transformation, and loading infrastructure using Spark, SQL, AWS, and Databricks. Develop entity resolution systems, CI/CD pipelines, anomaly detection, and high-quality data products. Design scalable processing and storage patterns, orchestration workflows, and data warehousing solutions while collaborating remotely and documenting technical designs.

Build and maintain scalable, fault-tolerant data pipelines, warehouses, and analytics solutions. Transform complex structured and semi-structured data, automate delivery processes, improve data quality and reliability, and support enterprise data strategies. Collaborate with analysts, data scientists, actuaries, and business teams to translate requirements into solutions. Research new technologies, troubleshoot issues, maintain version-controlled code, and mentor data engineering team members.

Designs and maintains scalable data models, semantic layers, ETL/ELT workflows, data-quality frameworks, and governed analytics products for commercial operations. Owns data solutions from requirements through deployment, monitoring, documentation, and support. Partners with technical and business stakeholders to deliver trusted reporting, dashboards, scorecards, productivity measurements, and AI-enabled decision support.

Designs, develops, maintains, and optimizes complex data systems, SQL databases, ETL processes, data warehouses, reports, and .NET applications supporting pharmacy benefit management. Responsibilities include database performance tuning, troubleshooting pharmacy claims processes, developing scalable solutions, documenting technical designs, participating in Agile ceremonies, collaborating with stakeholders, mentoring teammates, and ensuring healthcare data compliance.

Build and operate production data and streaming systems across edge, hybrid, OT, IoT, and enterprise environments. Responsibilities include developing connectors, real-time event-driven pipelines, data contracts, schemas, validation, lineage, observability, telemetry, federated query patterns, and reliable integration platforms supporting analytics and AI. The role requires strong ownership, system design, documentation, incident readiness, and collaboration with engineering and customer-facing teams.

Design, build, deploy, and maintain AWS-based data and AI/ML infrastructure for enterprise agentic AI platforms. Develop data lake pipelines, observability systems, agent-enablement tooling, and production MLOps/LLMOps capabilities. Own features end-to-end, contribute to architecture and design decisions, optimize reliability, scalability, security, and cost, and collaborate with AWS Professional Services, client teams, and junior engineers.

Designs and productionizes hybrid knowledge graph solutions for healthcare and clinical data using LPG and RDF technologies. Builds FHIR transformation and ingestion pipelines, implements ontology mappings, entity linking, identifiers, provenance, validation, and graph queries with Cypher or SPARQL. Collaborates with ontology, terminology, FHIR, clinical, and data engineering teams, while contributing to architecture reviews, code reviews, testing, and engineering best 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.