Top Senior Data Engineer Jobs in Denver & Boulder, CO

Design, build, and operate large-scale data platforms and Spark/PySpark pipelines. Enable data integration, modeling, quality, and observability. Build MCP servers and AI-augmented tooling, mentor engineers, and lead cross-functional projects to deliver reliable data products.

Design, build, and maintain scalable Spark-based ETL pipelines and computed tables in a central data lake. Integrate structured and unstructured IoT, sensor, and external data for analytics, model training, and dashboards. Collaborate with Data Science, Analytics, and ML teams to ensure reliable, high-quality customer-facing datasets.

The role involves architecting and improving data systems, defining governance standards, optimizing performance, and mentoring engineers in data engineering.

Design, develop, and maintain enterprise data integration (ETL) systems and data platforms. Lead technical designs, optimize performance and scalability, support production issues and off-hours rotations, build reporting and BI solutions, document data processes, and collaborate with stakeholders and vendors to align business requirements.

Design, develop, and optimize ETL/data integration, data platforms, and BI solutions. Lead technical designs, troubleshoot production issues, participate in on-call support, and document processes to ensure scalable, secure, high-performance enterprise data solutions.

Design, build, and optimize scalable ETL/ELT data pipelines using Databricks and cloud platforms. Implement data quality checks, monitor and troubleshoot jobs, audit pipelines for compliance, and collaborate with analysts and business stakeholders to define data models and support reporting.

Senior Exasol data engineer to design, build, optimize, and validate SQL-based data pipelines for healthcare analytics. Responsibilities include complex query tuning, extracting/staging from SQL Server, chart validation, applying clinical terminologies, quality measure development, and maintaining disciplined version-controlled engineering practices.

Lead design and development of web UI and REST middleware within an Agile team. Define requirements and acceptance criteria, implement Java/Spring and Angular solutions, build CI/CD pipelines, work with Product Owner and testers, and produce/update design documentation.

The Lead Platform Data Engineer will design data architecture, develop scalable workflows, ensure data quality, and mentor teams in modern DataOps practices.

Lead design and optimization of high-throughput data ingestion pipelines and Python-based microservices. Improve pipeline performance, reliability, and observability across distributed systems, define unified ingestion architectures, work with Snowflake for analytics, mentor engineers, and collaborate cross-functionally to ensure scalable, fault-tolerant data platforms.

Build and maintain low-latency, highly available distributed data systems and services to deliver high-quality data for product, analytics, and ML. Partner with data scientists and product teams to design scalable, observable, multi-tenant data products using batch and real-time processing frameworks. Ensure 24x7 reliability, monitoring, and operational excellence while owning end-to-end delivery.

Design, build, and own large-scale batch and streaming data pipelines and core Ads data products. Collaborate with analytics, data science, and engineering teams to model, transform, govern, and deliver high-quality ad datasets for analysis, ML, and reporting while ensuring privacy compliance.

Build and maintain scalable batch and real-time data pipelines, model data to power analytics and ML, and operate distributed data systems. Collaborate with business, engineering, and data science teams to ensure data quality, documentation, and debugging while adopting new technologies and working in an ambiguous, fast-changing environment.

Design, build, and optimize scalable Azure data platforms and lakehouse architectures. Develop ETL/ELT pipelines (PySpark/Spark SQL), enable Power BI datasets, implement CI/CD and IaC, ensure data governance/quality with Purview, support cloud migrations and production operations while collaborating with stakeholders and agile teams.

Lead design and delivery of scalable, secure cloud data pipelines and products. Translate requirements into architecture, apply advanced data modeling (Data Vault 2.0/dimensional), embed data quality, observability, security, and DataOps/CI-CD practices, optimize performance and cost, mentor engineers, and enable AI/ML-ready datasets within an agile DevSecOps model.

Design, build, and operate scalable cloud data pipelines and platforms (Azure preferred). Implement DevSecOps, CI/CD, automated testing, monitoring, performance tuning, and production readiness. Mentor engineers, drive operational excellence, and translate requirements into robust technical designs. Apply healthcare domain knowledge and collaborate in agile teams to deliver secure, reliable data products.

Design, build, and optimize scalable Azure data platforms and lakehouse architectures. Develop ETL/ELT pipelines (PySpark, ADF, Fabric), enforce data governance and quality, enable Power BI reporting, lead cloud migration and CI/CD, and provide production support and performance tuning across enterprise data systems.

Design and deliver Databricks lakehouse solutions: build ETL/ELT pipelines with PySpark and SQL, implement governance and Unity Catalog, integrate data platforms with applications via APIs/events, apply cloud architecture best practices, automate CI/CD for Databricks workloads, and consult with clients on requirements and solution design.

As a Data Engineer, you will design and maintain data pipelines, support DoD systems, and ensure data quality and compliance.

Lead design and hands-on delivery of a next-generation data platform and pipelines (batch, ELT, near-real-time) powering BI, experimentation, and ML. Own platform architecture, data modeling, governance, observability, and mentor engineers while modernizing legacy systems and driving AI-first data engineering practices across cross-functional teams.

Lead architecture and delivery of a cloud-native, petabyte-scale data platform: define technical roadmap, mentor engineers, build ETL/ELT pipelines, enable real-time streaming, implement Iceberg table format, optimize Snowflake/Redshift/Athena compute, and integrate observability with Splunk.

Lead end-to-end data engineering on GCP: design scalable data platforms and architectures (lake/lakehouse/warehouse), build and optimize BigQuery/Spark pipelines, ensure data quality/security, enable ML workflows with Data Science, mentor engineers, and own delivery and performance.

Design, build, and optimize scalable AWS-based data pipelines, data lakes, and warehouses. Migrate legacy systems, enforce data quality/security/governance, implement ETL/ELT and big-data solutions, adopt CI/CD and IaC, collaborate with stakeholders, and provide technical leadership and troubleshooting.

Design, build, modernize, and maintain operational and analytical data capabilities for Wealthscape Reporting, Analytics and Insights. Perform solution design, data analysis, ETL development, and production rollouts using Snowflake, AWS, cloud databases, and CI/CD pipelines. Collaborate across teams in a fast-paced financial services environment.

Design, build, and maintain ETL/ELT data pipelines and orchestration using Airflow, Python, and PySpark. Develop custom connectors, implement DataOps and CI/CD for data workflows, monitor and troubleshoot pipelines, validate data quality, and document processes while collaborating with cross-functional teams and supporting large-scale distributed data systems.