Top Senior Data Engineer Jobs in Denver & Boulder, CO

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.

Build and own scalable data architecture, canonical data models, configurable source integrations, and reliable pipelines for AI-driven financial intelligence. Use SQL, Python, dbt, cloud data warehouses, and orchestration tools to deliver idempotent, auditable data workflows. Establish lineage, data contracts, quality controls, and schema evolution practices. Lead architecture reviews, mentor distributed data engineers, and support customer-hosted multi-cloud deployments with strict tenant isolation and traceability.

Build and maintain dbt and semantic data models in Snowflake, develop ingestion and transformation pipelines, define trusted product metrics, and improve data collection. Partner with Product and Engineering to support experimentation and feature analysis. Establish data quality testing, documentation, HIPAA-aware access controls, and compliance practices. Use and validate AI-generated code and Snowflake Cortex outputs while raising technical standards across the team.

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.

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 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.

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.

Develop and optimize enterprise Oracle SQL and PL/SQL solutions, including data models, database designs, APIs, ETL processes, batch jobs, and performance tuning. Collaborate with cross-functional teams to translate requirements into scalable implementations, support testing and CI/CD, troubleshoot production issues, and document solutions. Provide technical leadership, mentor engineers, participate in architecture and code reviews, and drive engineering best practices across data modernization initiatives.

Design, build, and operate scalable ETL/ELT pipelines and backend data services using Go. Integrate relational, NoSQL, blockchain, API, web, and social data sources; develop NLP pipelines for text extraction and classification; and support production APIs, dashboards, alerts, and intelligence systems. Collaborate with engineering, data science, and investigations teams on architecture, data quality, lineage, validation, monitoring, and client-facing products.

Leads data engineering and data science teams, setting strategy and delivering scalable pipelines, data architecture, models, analytics infrastructure, and data governance. Partners with product, analytics, and business stakeholders to prioritize initiatives, assess upstream changes, maintain data quality, and support Snowflake-based warehousing. This hands-on leadership role includes mentoring staff, contributing to technical codebases, and advancing data science and engineering solutions.

Design, build, and maintain scalable batch and streaming data pipelines using Python, SQL, Kafka, and GCP. Develop REST and GraphQL data services, CI/CD automation, observability, and data-quality frameworks. Integrate ML and NLP models into production workflows, including continuous retraining and real-time inference. Support structured and unstructured healthcare datasets, distributed systems, microservices, and reliable data infrastructure while collaborating with data science and MLOps teams.

Build and configure a government Databricks workspace, develop ingestion and integration pipelines from enterprise and legacy systems, and prepare data for reporting. Implement Unity Catalog governance, metadata management, auditing, and lineage; configure MLflow workflows; assess security controls for sensitive data; and create architecture documentation, data dictionaries, and SOPs to support long-term sustainment.

Lead the design and optimization of healthcare data pipelines integrating EHR, payer, HIE, and vendor data. Build AWS-based ETL/ELT infrastructure, reusable integration tooling, data quality monitoring, and ML enablement foundations. Ensure HIPAA-compliant handling of clinical data, including security controls, audit logging, encryption, and de-identification. Provide technical leadership, mentor engineers, influence platform architecture, and lead complex partner integrations involving FHIR, HL7v2, CCDA, and healthcare terminology standards.

Architect, build, and operate scalable data platforms processing billions of daily events through real-time and batch pipelines. Own production reliability, data quality, security, observability, and performance while partnering with engineering, analytics, data science, growth, and business teams. Develop frameworks and datasets, make architectural decisions, evaluate technologies, mentor engineers, and help define the platform roadmap.

Design and lead scalable, secure, highly available database infrastructure for multi-terabyte SQL Server environments. Set technical direction for data architecture, performance, real-time pipelines, security, and growth. Troubleshoot complex production issues, optimize queries and indexes, guide cross-functional projects, mentor engineers, review changes, reduce technical debt, and evaluate emerging technologies. Monitor database health using Splunk, Datadog, and Grafana while influencing engineering and Site Reliability strategy.

Build and operate a large-scale enterprise data platform spanning batch and streaming processing, lakehouse storage, Kafka infrastructure, APIs, observability, and agentic MCP interfaces. Develop reusable ingestion and data-quality frameworks, improve scalability and resilience, establish engineering practices, support production operations, and mentor engineers. The role requires deep ownership of distributed data infrastructure and practical use of AI coding agents.

Design and operate production-grade data pipelines, backend services, APIs, and agentic AI workflows. Build scalable systems that ingest, transform, and operationalize data for customer-facing and internal applications. Translate ambiguous operational workflows into reliable architectures, improve service observability and performance, and own technical decisions across data modeling, system design, security, and compliance.

Own and expand Chicory’s analytical data platform supporting advertising, campaign, financial, product, and publisher reporting. Design BigQuery architecture, build batch and streaming pipelines with Python, SQL, Beam, Dataflow, and Airflow, and develop governed data marts and BI datasets. Establish data quality, observability, reconciliation, access controls, cost management, and engineering practices. Partner with engineering, finance, product, campaign management, sales, and leadership while leading incident recovery and documenting systems.

Build scalable, secure data pipelines, models, platforms, and governance systems that enable AI applications to access trusted enterprise data. Partner with analytics, marketing, and product teams to translate requirements into technical solutions, independently deliver projects through production, and support sensitive-data security and access controls. Preferred experience includes LLM architectures, cloud data platforms, infrastructure as code, regulated environments, and technical leadership.

Lead technical design and hands-on development of Pantheon’s data platform, extending ingestion, curation, retention services and ML/LLM infrastructure. Mentor engineers, drive architecture and engineering standards, operate in a DevOps model, partner cross-functionally, and participate in on-call support to ensure reliable, compliant data access and analytics.

Design and scale lakehouse architecture, streaming and batch pipelines, and reliable data platforms using Kafka, Spark, Airflow, Iceberg, Databricks, and related technologies. Build Medallion-layer data systems, manage open table formats, optimize distributed queries, monitor platform reliability, resolve data quality issues, and collaborate with analysts, data scientists, and product teams.

Provides hands-on technical leadership for foundational data engineering and analytics engineering. Owns multi-year data architecture, production pipelines, dimensional models, and trusted metrics spanning cloud costs, traffic, infrastructure, and AI usage. Designs new datasets integrating cost, utilization, performance, and reliability signals; establishes standards, coaches engineers, aligns stakeholders, and drives adoption across Infrastructure, Finance, Data Science, and Product Engineering.

Design and implement scalable analytics/data models and dbt pipelines, manage Databricks, build dashboards and BI solutions, partner with business stakeholders, support CRM/marketing analytics, and explore GenAI/ML opportunities to deliver reliable, automated data products.

Own and evolve production data systems supporting healthcare, claims, billing, analytics, and reporting. Design resilient ingestion patterns, data contracts, models, schemas, and pipelines that handle incomplete, duplicated, delayed, and changing data. Improve performance, observability, failure handling, retries, backfills, and replay safety. Collaborate with engineering, product, operations, and analytics teams while making architecture decisions and documenting technical changes.