Top Senior Data Engineer Jobs in Salt Lake City, UT

The Senior Data Engineer will design and implement data processing pipelines using Java and Spark, optimize performance, and mentor junior engineers while ensuring compliance with data governance standards.

Lead design and delivery of scalable production data pipelines and data products (Spark/Python/SQL) on Databricks; integrate diverse federal health systems, define reusable ingestion patterns, ensure data quality/governance, mentor engineers, and troubleshoot/optimize production workflows.

Design, build, and operate scalable batch and streaming data pipelines and a lakehouse on AWS. Implement ETL/ELT, CDC, metadata/cataloging, lineage, governance, data quality, IaC, and CI/CD for reliable, observable, and secure data platforms to support analytics and ML.

Build and maintain scalable ETL/data pipelines and internal frameworks to support ML and product teams. Drive architecture, observability, data quality, and engineering best practices across AI/ML workflows and prompt-engineering pipelines.

Lead design, build, and operate scalable Databricks/Delta Lake data pipelines and Kimball dimensional models using dbt. Manage governance with Unity Catalog, optimize performance and cost, operationalize ML with MLflow, and coordinate/quality-check offshore vendor engineering. Mentor engineers, uphold standards, and deliver trusted data products that power analytics, reporting, and MLOps workflows.

Design, build, optimize, and modernize enterprise Microsoft data platforms (Azure, Fabric). Develop ETL/ELT pipelines, data warehouses/lakehouses, implement governance, security, DevOps/CI-CD, and support Power BI and AI initiatives while consulting with stakeholders.

Design, build, and maintain scalable data pipelines, data models, ETL/ELT, and BI solutions. Collaborate with data scientists and stakeholders to improve data quality, accessibility, and PHI security. Develop and document workflows, optimize processing/storage, support data warehouse and visualization, and adopt AI tools to drive data-informed outcomes.

Design, build, and operate scalable data transformation pipelines and dimensional models using dbt, SQL, and AWS. Implement infrastructure-as-code (AWS CDK), CI/CD, and query performance optimizations while ensuring data quality, governance, and collaboration with cross-functional and offshore teams.

Lead Data Engineer builds and maintains scalable data pipelines and lakehouse/warehouse platforms (Databricks) to support analytics, BI, reporting, and AI. Responsibilities include data modeling, ETL/ELT, medallion architecture, orchestration and monitoring (Airflow/Cron), performance tuning, CDC/incremental loads, data quality, and leading/mentoring a team while collaborating with stakeholders and maintaining documentation.

Architect, build, and maintain Snowflake-based ELT pipelines and production data models. Implement observability, data quality, and governance; optimize cost and freshness. Partner with Analytics, Product, and Engineering, use dbt/CI-CD and AI-assisted tooling, and participate in reviews and architecture planning to support analytics, BI, and ML.

Design and implement data federation and lakehouse architectures, build scalable ETL/ELT pipelines with Python and Spark, optimize performance across federated queries, manage Delta/Iceberg/Hudi tables, and enforce governance, security, and access controls for analytics and AI teams.

Design, build, test, and maintain high-performance Python data applications and backend services. Lead technical design, review code, mentor junior engineers, integrate with databases and cloud services, optimize performance, and ensure security, scalability, and reliability.

Design, develop, maintain, and optimize enterprise Tableau dashboards and BI solutions. Integrate and cleanse data from Oracle/Sybase/Big Data, tune SQL, automate Tableau extracts, manage Tableau Server on Linux, support testing, UAT, deployments, and troubleshoot data integrity and performance issues.

Design, build, and maintain scalable batch and streaming data pipelines, improve data modeling and governance, contribute to enterprise data architecture, and collaborate with cross-functional teams to ensure reliable, high-quality data for analytics and operational use.

Design, build, and maintain an internal data platform enabling secure, governed access for engineering and analytics. Partner with architects, implement data pipelines and automations, enforce security and compliance, support platform services, recommend architecture improvements, mentor teams, and drive adoption of scalable, self-service data capabilities.

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.

Lead a team of data engineers to design, build, and maintain scalable data pipelines, a Data Vault, data warehouses and marts. Own backlog, sprint planning, mentoring, stakeholder collaboration, data validation/monitoring, and platform improvements to deliver key metrics and data infrastructure across the business.

Design, deploy, and maintain scalable data infrastructure and orchestration for analytics and ML; write optimized SQL over large warehouses; partner with engineering and product to structure source systems; maintain CDC pipelines; scale reporting and expose analytical datasets enterprise-wide while ensuring data management, security, and compliance.

Design and own end-to-end data platform architecture, build ingestion and transformation pipelines, model multi-tenant datasets, optimize Postgres and ClickHouse performance/cost, implement orchestration and observability, define data quality and reconciliation standards, and mentor data/product engineers.

Design, implement, and manage scalable multi-cloud data platforms and ELT pipelines (AWS/Azure). Build and optimize Snowflake data models, dbt transformations, and Airflow orchestration. Define data engineering standards, enforce governance and quality, collaborate cross-functionally, and mentor global/offshore teams.

Build and maintain automated ETL/ELT data pipelines, improve data quality and monitoring, optimize Postgres and BigQuery schemas and queries, develop internal tooling, support data governance (HIPAA), participate in on-call rotation, and collaborate across engineering teams to scale data systems.

The Data Engineer will design and maintain data pipelines, enhance the data warehouse, support machine learning applications, and collaborate with various teams to leverage data effectively.

The Senior Data Engineer will build and maintain ETL/ELT pipelines, optimize SQL queries, and collaborate on machine learning workflows, driving data engineering best practices.

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 will design and maintain data pipelines, write production-grade Python and SQL code, and support AI initiatives while collaborating across teams.