Top Data Engineer Jobs in Salt Lake City, UT

As a Senior Associate in Data Engineering at PwC, you will design and implement data architecture strategies, analyze complex issues, mentor junior members, and build client relationships while ensuring adherence to project standards.

As a Senior Manager in Data Engineering, you will lead teams to design data architectures, develop data solutions, manage client engagements, and ensure compliance with data governance policies.

Lead data engineering projects, develop and implement data pipelines and architecture strategies, manage data quality, and mentor team members at PwC.

Build, model, and maintain scalable BigQuery-based data solutions on GCP. Implement performant data models, storage partitioning/clustering, ETL improvements, data validation, and documentation. Collaborate with architects, data scientists, and engineers to deliver governed, high-quality data for reporting and AI initiatives.

As a Data Engineer, you'll lead data engineering projects, develop data transformations, build workflows with Apache Airflow, and ensure data quality and integrity while collaborating with stakeholders.

Design and build data infrastructure, pipelines, and integration solutions using cloud and big-data tools. Develop data lakes/warehouses, ensure data quality and security, apply data modeling and DAGs, use Databricks, Airflow, and Hadoop, and collaborate with clients to deliver actionable insights.

The Data Engineer will build and maintain data pipelines, optimize workflows, manage infrastructure, and enhance pipeline observability while collaborating with teams for effective data delivery.

Design, build, and automate scalable data platforms (lakehouses/warehouses). Implement and optimize ETL/ELT pipelines using Databricks and dbt, apply Medallion Architecture, manage IaC/CI-CD, ensure data security and governance, and integrate pipelines with BI tools while engaging with stakeholders.

Build, maintain, and test data pipelines and ETL/ELT processes using Python and SQL; integrate Kafka streams; model data in Snowflake; implement CI/CD (GitHub Actions), logging, observability, and on-call support. Collaborate with senior engineers and application teams to enable self-service data ingestion and improve pipeline reliability and data preview capabilities.

Lead data engineering efforts within Technology Consulting: design data architecture and pipelines, implement AWS/Redshift and ETL solutions, support BI (QlikView/Oracle BI), coach teams, manage client relationships and SLAs, apply systems thinking to optimize outcomes and validate solutions with stakeholders.

Build and maintain cloud cost monitoring, alerting, and optimization tooling. Develop automation, cost allocation/tagging, data pipelines, dashboards, and integrations with billing APIs. Support FinOps processes, implement cost guardrails, troubleshoot cost systems, participate in on-call rotation, and collaborate with senior engineers and product teams to drive cost-efficient infrastructure and reporting.

Lead the evolution of data platform by designing scalable pipelines, improving data processes, mentoring teams, and modernizing data architecture.

Own and architect large-scale data pipelines, orchestration, and integrations. Drive IaC strategy (Terraform), third-party API and AI/LLM tool integrations, build export pipelines and internal tooling, mentor engineers, and align cross-team technical strategy for reliable, scalable data infrastructure.

Own SQL and dbt-based data modeling and data quality for clinical and claims datasets. Author and refactor dbt models/tests, triage DQ alerts, design monitors, optimize SQL performance, and collaborate with Data Engineering and stakeholders to ensure trusted production datasets.

The Data Engineer will develop and implement ETL processes, design data architectures, work with data platforms, and ensure data solutions meet scalability and security needs. Responsibilities include mentoring junior engineers and collaborating with stakeholders to gather requirements and optimize data transformations.

Design, develop, and monitor large-scale data pipelines and cloud-native data applications; collaborate with consulting teams and clients; apply best practices (TDD, CI/CD); build and operationalize ML-ready data platforms; produce documentation and runbooks; mentor junior engineers and contribute to proposals and analytics assets.

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.

Support a Department of State implementation by assessing data policies and architecture, developing data pipelines, cleaning and integrating datasets (using Palantir Foundry), and producing analyses and visualizations. Create governance documentation, catalog data fidelity issues, coordinate with non-technical users, and recommend improvements to meet analytical and mission goals.

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).

No job description, responsibilities, or qualifications were provided. Title suggests a combined Data Engineer and Business Analyst role, likely involving data pipelines, ETL, data modeling, and business requirements/analytics, but specifics are not available.

Design, build, and maintain cloud-based ELT data pipelines and analytics-ready datasets using Snowflake, dbt, and integration tools (Fivetran/Airbyte). Implement CDC and incremental loading, optimize Snowflake performance, enforce data quality and monitoring, create dimensional models for reporting, enable CI/CD for dbt projects, and collaborate with analytics and stakeholder teams to support reporting and advanced analytics.

Design, build, and maintain scalable data pipelines, data lakes, and databases; ingest and map customer financial datasets; monitor pipeline reliability; translate business requirements into data flows and analytical insights to ensure high-quality, usable data.

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

Design, build, and operate scalable cloud data pipelines and platforms (development through production). Embed security, automation, CI/CD and testing; monitor, tune, and resolve incidents; provide technical design, mentoring, and cross-functional collaboration to deliver reliable healthcare-focused data products.

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