Top Senior Data Engineer Jobs in Salt Lake City, UT

Design, build, and maintain data models, pipelines, and access layers to support care delivery, operations, and analytics. Ensure data quality, governance, and reliability while optimizing infrastructure and performance. Serve as a technical leader, review code, set engineering best practices, and collaborate cross-functionally to translate business needs into scalable data solutions.

Lead architecture and implementation of cloud-native data platforms on Google Cloud (BigQuery). Build unified batch/streaming pipelines, enforce IaC with Terraform, implement open table formats and governance (Dataplex/Data Catalog), automate data quality/lineage, partner with analytics and product teams, and mentor data engineers.

The Data Architect/Data Governance Lead supports the Medicaid Enterprise Data Warehouse by leading data architecture, metadata management, and governance processes, ensuring compliance and scalability.

As a Sr. Data Engineer at Lyra Health, you will build and scale core data infrastructure, design data warehouses, and create efficient data pipelines while collaborating across departments.

The Senior Data Engineer leads the design and optimization of scalable data pipelines for AI-driven analytics while collaborating with cross-functional teams. Responsibilities include ensuring data integrity, automation, and compliance in cloud environments, particularly on Google Cloud Platform, to enhance decision-making processes.

In this role, you will design data architecture, lead integration strategies, mentor engineers, and drive AI infrastructure for cross-division data systems.

Lead the design of DataOps platforms, focusing on scalable data infrastructures and automation frameworks. Oversee robust data orchestration and establish quality frameworks.

Design, build, and automate scalable batch and streaming data pipelines, implement data models and ETL, automate deployments, support ML/experimentation, maintain server architecture, and partner with stakeholders to deliver analytics and data-driven tools.

As a Senior Data Engineer, you'll transform raw data into datasets, design production pipelines, integrate customer data, and collaborate with various teams to ensure data quality and reliability.

The Senior Data Engineer will design and implement lakehouse architectures, optimize ClickHouse deployments, develop ETL/ELT pipelines, and create data models to support analytical and operational functions.

Lead design and delivery of large-scale data pipelines and warehouse systems. Define technical strategy, enforce data governance, improve developer efficiency, mentor engineers, and align data engineering efforts with business goals to enable analytics and ML at scale.

Lead design and scaling of enterprise data architecture and pipelines. Build and maintain ETL/ELT and CDC/streaming workflows, improve data quality and observability, own schema and warehouse modeling, implement governance (RBAC), and provide cross-functional technical leadership to enable trusted, scalable data for analytics and product teams.

The Senior Data Engineer will design and build data pipelines and infrastructure to support data science and business intelligence solutions, utilizing AWS services and streaming technologies.

Build, maintain, and optimize cloud data pipelines and dbt models; design Airflow DAGs; implement dimensional models and BI dashboards; integrate healthcare data with HIPAA-compliant practices; contribute to RAG pipelines and MLOps workflows; review contractor code, monitor data quality, and document designs following governance standards.

As a Senior Data Engineer, you will design and optimize data pipelines, enhance system efficiency, and collaborate with teams to drive health outcomes using large healthcare datasets.

As a Senior Data Engineer, you will enhance the data lakehouse, optimize data workloads, perform complex transformations, and develop frameworks to improve delivery velocity, focusing on cybersecurity solutions.

Design, build, and scale reliable batch and real-time data pipelines and warehouses. Partner with engineering, data science, and business teams to define data models, improve observability, optimize storage and querying, and evangelize data engineering best practices across the company.

Lead data engineering for a healthcare scheduling platform rebuild: map legacy multi-TB schema, design target schema, build translation layer (views/materialized views and write mappers), ensure data consistency during parallel systems, plan per-tenant cutover with verification and rollback, tune DB performance, instrument data quality/lineage/audit, and mentor engineers.

Design and build scalable batch and streaming data pipelines, lead architecture for Lakehouse platforms, and mentor junior engineers while optimizing data quality and performance.

Design, build, and maintain scalable Databricks-based data pipelines and ELT workflows; develop and document dbt models; define data modeling methodology; ensure data quality, reliability, and scalability; collaborate with clients and cross-functional teams; provide technical guidance and deliver improvements to clients' data infrastructure and analytics capabilities.

As a Senior Data Engineer, you will manage data pipelines, ensure data quality, partner with teams for data-driven decisions, and improve the data platform while maintaining data integrity.

The Senior Data Engineer will create and manage data pipelines, ensuring data quality, collaborating with teams for data-driven decisions, and evolving the data platform.

The Senior/Staff Data Engineer will design and scale the core data platform for healthcare systems, focusing on infrastructure, tooling, and governance while mentoring engineers and collaborating with various teams.

The Senior Data Engineer will design and build scalable data platforms, handle automation for data operations, and ensure reliability and performance of data pipelines while collaborating with various teams.

Build, operate, and modernize scalable data pipelines and warehouse environments for lottery reporting, analytics, and AI readiness. Improve ingestion, transformations, data models, contracts, observability, lineage, and onboarding patterns. Automate operations, troubleshoot production issues, mentor engineers, and support migration from legacy to cloud architectures while ensuring data quality, reliability, and regulatory continuity.