Top Data Engineer Jobs in Denver & Boulder, CO

Architects, deploys, and operates scalable data platform infrastructure supporting analytics. Responsibilities include administering Airflow and Superset, managing Terraform infrastructure, building CI/CD pipelines, enforcing security and compliance, developing DBT data models, cataloging assets in OpenMetadata, writing Python automation, and mentoring engineers. The role requires extensive infrastructure and data engineering experience across cloud platforms, containers, data security, and workflow orchestration.

Build and optimize scalable batch and streaming data pipelines using Python, PySpark, SQL, and Apache Spark on AWS. Develop EMR data processing jobs, lead an Apache Airflow orchestration proof of concept, integrate Amazon Q for data quality and documentation, and implement GitLab CI/CD with infrastructure-as-code. The role emphasizes data governance, security, automation, performance, scalability, and cost efficiency while collaborating in Agile teams and presenting proof-of-concept findings.

Develop and optimize cloud-based data pipelines for analytics, machine learning, LLM feature stores, and AI applications. Build AI agents, integrate large language models and vector datasets, implement monitoring and self-healing workflows, and establish data observability for freshness, lineage, and performance. The role also structures semantic data layers, improves resource utilization and cloud costs, troubleshoots distributed platforms, and collaborates across teams to align data infrastructure with business needs.

Build and operate production data pipelines and dimensional models using Spark, SparkSQL, and cloud lakehouse technologies. Own pipelines from requirements through deployment, monitoring, and iteration; improve data quality, lineage, reliability, and cost efficiency. Partner with data scientists, analysts, product managers, and engineers to support datamarts, KPIs, reporting, and analysis. Participate in business-hours on-call rotations and improve runbooks and alerting.

Build and govern Ibotta’s foundational data platform using Python, SQL, Terraform, Databricks, AWS, and Spark. Develop infrastructure provisioning, access controls, governance automation, self-service tooling, and scalable data pipelines. Support reliability through incident resolution, root cause analysis, on-call participation, and platform improvements. The role requires three years of software or data engineering experience and is hybrid in Denver.

Lead design and implementation of enterprise data architecture and cloud-based data solutions. Manage large data engineering projects, develop data models and pipelines, ensure governance and security compliance, advise clients strategically, and build high-performing inclusive teams.

Administer and enhance Edmunds’ Databricks data platform and AWS infrastructure. Build and maintain ETL pipelines, infrastructure-as-code tooling using Terraform or CDK, and operational dashboards, alerts, and reports. Collaborate with business, engineering, analytics, security, and infrastructure teams to support data platform users and AI solutions. Evaluate new technologies, troubleshoot platform issues, and improve operational, cost, and security visibility.

Define and govern enterprise-scale data architecture across batch, streaming, warehouse, lakehouse, transactional, and AI use cases. Establish standards for data quality, lineage, access, cataloging, governance, observability, and SLAs. Architect AI-enabled workflows, resolve complex architecture issues, influence roadmaps, and mentor engineers through hands-on technical leadership. The role requires 15+ years of software, data engineering, or architecture experience and expertise in large-scale data platforms and modeling.

Build and maintain large-scale data processing pipelines using Apache Spark (PySpark/Scala) and Python. Design ETL workflows, implement distributed data processing, use version control (Git), and work with cloud platforms (GCP/AWS/Azure).

Build and maintain enterprise data pipelines and integration workflows across on-premises and cloud systems. Ingest, transform, and stage structured and semi-structured data; write and optimize SQL queries, views, and stored procedures; implement data validation, monitoring, alerts, and logging; document pipeline logic; use source control and code reviews; and provide support through tickets and on-call rotations.

Designs and maintains data warehouse infrastructure, semantic and dimensional models, metrics layers, dashboards, reporting, and secure data pipelines. Builds governed self-service analytics in Sigma, Power BI, and Databricks; defines KPIs and data standards; implements row-level security and Entra ID controls; supports data quality, observability, AI/ML use cases, and BI adoption. Collaborates with technical and business stakeholders in an Agile-Scrum environment.

Build and maintain production data ingestion pipelines, transformation models, warehouses, and schemas for PerfectServe’s data platform. Deploy reliable, tested pipelines using idempotency and data-quality checks, apply dimensional and medallion modeling, participate in design reviews, and document owned pipelines and runbooks. The role requires SQL, programming experience, dbt or similar transformation tools, cloud data warehouse experience, ELT orchestration, AWS knowledge, and a strong data-testing mindset.

Designs, builds, and maintains enterprise data pipelines, ETL processes, integrations, data infrastructure, and complex data models. The role supports analytics partners, develops scalable data warehouse solutions, tunes SQL and databases, troubleshoots production issues, and collaborates with architects, analysts, developers, and stakeholders. Responsibilities include end-to-end project delivery, data modeling, big data engineering, production support, agile planning, and mentoring team members.

Own end-to-end data workflows, including ingestion, storage, orchestration, transformation, validation, and delivery. Build ETL/ELT pipelines, manage cloud data platforms and transfers, optimize data models and queries, maintain data quality monitoring, troubleshoot production issues, and document data flows. Collaborate with technical and nontechnical teams to investigate discrepancies, support enterprise datasets, and apply secure data-handling practices.

Develop and automate data workflows, analytical tools, visualizations, and AI/ML capabilities supporting engineering, product design, sales, and manufacturing. Translate business requirements into automated solutions, monitor production datasets, improve analytics workflows, collaborate with IT and cross-functional teams, and communicate analytics value to stakeholders. The role also provides data expertise and documentation across the organization.

Build and maintain data pipelines, ingestion workflows, transformations, and data products using Python, SQL, and cloud data platforms. Test and troubleshoot systems while applying Git, software testing, and CI/CD practices. Collaborate with senior engineers and cross-functional stakeholders, use AI-assisted tools, and deliver reliable data solutions in a fast-paced startup environment.

Build and maintain production ETL/ELT pipelines, data models, and curated datasets for analytics and AI applications. Ingest data from databases, APIs, SaaS tools, and event streams; implement quality checks, monitoring, documentation, governance, and access controls. Collaborate with analysts, data scientists, and ML engineers on reporting, feature datasets, AI search, and RAG applications. Optimize query performance and pipeline costs while using Git, code reviews, and CI/CD for safe releases.

Build and maintain Snowflake data models using dbt and orchestration tools, improve data quality and observability, define data contracts, and support a modular healthcare data platform. Collaborate with analysts, data scientists, and software engineers to deliver trusted datasets. Monitor and troubleshoot pipelines, optimize Snowflake performance and costs, participate in design reviews, document systems, and support governance, security, and privacy standards for healthcare data.

Build, test, document, and optimize Snowflake data models using dbt. Improve data quality, observability, lineage, and data contracts while collaborating with analysts, data scientists, and software engineers. Monitor pipelines, troubleshoot incidents, optimize query and storage performance, support client-specific configurations, participate in design reviews, and contribute to scalable data platform patterns. The role also requires clear documentation and awareness of security, governance, privacy, and healthcare data requirements.

Build and operate event-driven data pipelines processing healthcare claims data for fraud, waste, and abuse detection. Operationalize validated fraud signals, maintain alert delivery and partner dashboards, monitor pipeline reliability, and collaborate with data architects, scientists, and investigative teams. The role requires production-scale pipeline engineering using AWS, Snowflake, Python, and SQL in a fully remote environment.

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.

Build and maintain production data pipelines and foundational datasets for Market Intelligence. Automate survey ingestion, cleaning, joining, scheduling, monitoring, and reporting-layer integration. Develop repeatable queries, improve data quality practices, document assumptions, support reproducible outputs, and translate research needs into technical solutions. The role requires close collaboration with technical and nontechnical stakeholders and ownership of reliable data infrastructure.

Build and operate platform infrastructure for product analytics, data processing, and ML systems. Responsibilities include event tracking pipelines, Databricks Bronze-to-Gold lakehouse layers, schema evolution, data contracts, CI validation, backfills, monitoring, feature computation, model pipelines, lineage, PHI policy enforcement, and self-service developer tooling. The role focuses on making data and ML changes safe, observable, reproducible, and accessible to engineers and subject-matter experts.

Design, build, and scale data pipelines, ETL workflows, analytics infrastructure, and machine-learning data systems. Ensure data quality, security, monitoring, and reliability while optimizing storage and processing performance. Collaborate with engineering, product, and analytics teams on data requirements, modeling, schemas, and scalable infrastructure. The role also establishes data engineering best practices and supports a data-driven culture.

Designs and maintains scalable data pipelines, architectures, models, and governance frameworks supporting analytics and machine learning. Integrates enterprise data, monitors quality, performs validation and root-cause analysis, and develops fault-tolerant systems handling massive data volumes. Collaborates with stakeholders to translate business needs into cloud and big data solutions using SQL, Python, Spark, Airflow, and related technologies.