Top Senior Data Engineer Jobs in Salt Lake City, UT

Design, build, and maintain data lakes, data warehouses, and end-to-end data pipelines for a multi-tenant SaaS platform. Lead code and design reviews, write infrastructure-as-code, optimize performance and scalability, participate in data governance, mentor junior engineers, evaluate new technologies, automate tests, collaborate cross-functionally, and join on-call rotation to ensure reliable data delivery.

The description combines recruitment management, event planning, business development consulting, and iOS application development. Responsibilities include developing recruitment strategies, training recruiters, maintaining candidate databases, coordinating events, managing vendors and client relationships, developing sales pipelines, and designing, testing, updating, and maintaining iOS applications.

The description is inconsistent with the title and primarily covers recruitment management, event planning, business development, and iOS application development. Responsibilities include developing recruitment strategies, managing recruiters and candidate databases, coordinating events and vendors, creating business development plans, managing client relationships, and designing, testing, and maintaining iOS applications.

Designs, implements, maintains, and evaluates environmental databases for CMS Unified Case Management. Builds and maintains ETL pipelines using AWS, Snowflake, and PostgreSQL; implements automated data validation; automates reporting pipelines; creates dashboards, tables, plots, maps, and documentation. Performs data analysis, develops recommendations, follows project specifications, and presents findings to stakeholders while applying Agile practices.

Designs and maintains pipelines migrating financial data from legacy Oracle databases to Oracle ERP. Develops SQL and PL/SQL transformations, backend integrations, data mappings, reconciliation, testing, monitoring, troubleshooting, and documentation. Partners with accounting, business, and technical stakeholders to resolve issues and support large-scale financial transformation. The role also involves AI tool adoption, code reviews, solution design, deployment activities, governance, communication, mentoring, and project oversight.

Own end-to-end data engineering for a business domain, including API and other-source ingestion, dbt models, orchestration, data-quality testing, monitoring, documentation, and performance. Build reliable, AI-ready single sources of truth and ensure data freshness, accuracy, traceability, and scalability. Collaborate with stakeholders and the Director of Data, establish engineering patterns, review work, and mentor other engineers.

Build and operate trusted data products, governed datasets, scalable pipelines, and platform workflows supporting analytics, AI, and business operations. Responsibilities include data quality, validation, observability, recoverability, semantic consistency, governance controls, permissions, privacy-aware access, automation, and production monitoring. The role partners with stakeholders to resolve ambiguous data needs, drives platform improvements, provides technical direction, and adopts agentic development workflows.

Build and maintain Snowflake and dbt data platforms, including ELT pipelines, tested models, orchestration, monitoring, documentation, access controls, and governance. Optimize performance and costs while ensuring reliable, discoverable data for internal dashboards, client products, and AI/ML use cases. Translate ambiguous stakeholder needs into durable solutions and apply strong software engineering practices, including Git, CI/CD, code review, Python, testing, and alerting.

Owns the design, development, deployment, and support of B2B data pipelines using BigQuery, dbt, Python, and orchestration frameworks. Translates client requirements into scalable data models, integrates CRM and marketing data, builds measurement and activation pipelines, and ensures quality through testing, validation, monitoring, and observability. The role also standardizes reusable B2B data capabilities, troubleshoots production issues, and supports account-level marketing-to-revenue measurement across multiple clients.

Design, build, and operate production data pipelines using Python, Spark SQL, and Airflow. Canonicalize healthcare and customer data into trusted internal models, optimize distributed processing, enforce schemas, and implement quality checks and observability. Support customer onboarding, streaming pipelines, troubleshooting, on-call operations, and platform best practices while collaborating with product, analytics, engineering, data science, and customer teams.

Design, build, and own business-critical data infrastructure, including AWS Glue and Spark pipelines, cloud warehouse or lakehouse systems, orchestration, and Tableau BI platforms. Define event-driven contracts, data models, schemas, observability, and batch or streaming architectures across multi-tenant, multi-region environments. Collaborate across producer and consumer teams while owning production data infrastructure and outcomes end to end.

Design and support ETL, data migration, transformation, import, export, and validation workflows for moving legacy EHR data into athenahealth systems. Build Python and SQL utilities, define source-to-target mappings, support HL7 and FHIR interoperability, validate EHI exports, investigate data quality issues, document processes, and collaborate with product, implementation, operations, sales, and engineering teams in a remote environment.

Design, build, and operate scalable PostgreSQL databases and cloud-native data infrastructure. Build and maintain ETL/ELT pipelines, optimize query and server performance, ensure high availability and disaster recovery, improve observability, troubleshoot production issues, guide schema and application design, evaluate database technologies, and mentor junior engineers to support reliable, secure data-driven applications.

Own the go-to-market data infrastructure, integrating HubSpot, Dreamdata, advertising platforms, PostHog, and website data into BigQuery. Diagnose tracking and attribution discrepancies, configure multi-touch revenue attribution, and build executive dashboards covering channel performance and revenue. Develop ABM workflows using Clay, enrichment, behavioral signals, and AI personalization. Partner with sales and marketing to improve funnel visibility, campaign decisions, and outbound effectiveness.

Own data ingestion, validation, reconciliation, and quality pipelines using Python, advanced SQL, and PostgreSQL. Design schemas, indexes, migrations, automated tests, and CI/CD workflows while handling unreliable third-party feeds. Develop test plans, track defects, establish quality metrics, conduct performance and security testing, and support compliance documentation. Preferred experience includes AWS event-driven architectures, healthcare or payer data, orchestration tools, infrastructure as code, FedRAMP environments, data quality frameworks, and anomaly detection.

Designs, develops, and maintains Databricks-based ETL pipelines, data lake architectures, datasets, and data quality processes supporting reporting, analytics, and AI/ML workloads. Maintains and migrates legacy SSIS and SQL Server systems, optimizes pipeline performance, and supports complex SSRS reporting. Collaborates with business users, analysts, and IT teams to define data requirements, troubleshoot issues, document solutions, and advance AI-assisted engineering practices.

Designs, develops, and maintains Databricks ETL pipelines, data lake architectures, datasets, and data integrations supporting reporting, analytics, and AI/ML workloads. Troubleshoots and optimizes Databricks and SSIS processes, maintains legacy systems, implements data quality controls, and supports data warehousing. Maintains SSRS reports and collaborates with business users, analysts, and IT teams to deliver reporting solutions. Documents work, communicates technical information, and contributes to AI-assisted development practices.

Build scalable data pipelines, ELT processes, infrastructure, and dimensional data models supporting internal analytics and customer-facing data products. Implement data quality monitoring, auditing, alerting, warehouse optimization, and governed semantic layers for AI and LLM-enabled analytics. Partner with technical and business stakeholders, improve data processes using AI, and deliver reliable metrics from diverse data sources.

Design, build, and optimize enterprise ETL/ELT pipelines, cloud data lakes, lakehouse solutions, data models, and semantic layers across AWS and Azure. Ensure data quality, security, performance, reliability, and governance while supporting analytics, reporting, and AI initiatives. Collaborate with product, engineering, IT, and business stakeholders to translate requirements into scalable data solutions, monitor pipeline health, troubleshoot issues, and improve data architecture and practices.

Lead data engineering and machine learning initiatives by designing, architecting, developing, and supporting GCP data pipelines. Create information architecture documentation, estimate and plan technical work, collaborate with project teams and business analysts, and translate requirements into technical designs. Develop Python-based ELT pipelines, optimize BigQuery SQL, support CI/CD, and provide technical leadership to projects and teams.

Design, build, deploy, and operationalize scalable data engineering solutions for customers. Responsibilities include developing pipelines, transforming and cleansing data, analyzing sources, implementing governance, security, and quality practices, testing data movement and transformation code, resolving defects and incidents, and advising customers on technical decisions. The role also supports AI, machine learning, business intelligence, and data product adoption while collaborating across teams and educating end users.

Designs and develops enterprise cloud data solutions, including batch, near-real-time, and API-based pipelines. Builds data models, warehouses, lakes, and lakehouses while implementing governance, quality, lineage, and curation practices. Supports CI/CD, testing, monitoring, observability, security, and compliance. Collaborates with architects, product owners, analysts, and leadership on platform strategy and modernization. Mentors engineers, leads reviews, presents technical topics, and explores AI-enabled data capabilities.

Lead Salesforce data migration and quality activities for a Department of Veterans Affairs modernization program. Analyze SQL Server data, create source-to-target mappings, execute ETL and Salesforce loading, validate and reconcile records, implement data quality controls, and develop migration and application test plans. Track defects, support automated monitoring and testing, collaborate with architects, developers, stakeholders, and provide migration status, risk, and quality updates.

Build and own the data platform backbone: design ingestion patterns, Snowflake warehouses, governance, and freshness strategy. Implement reliable batch and streaming pipelines, data quality, observability, and onboarding processes. Lead legacy migrations, create data contracts and SLAs, and apply AI-native tooling to automate debugging, documentation, and operational workflows while partnering with Analytics, Engineering, and vendors.

Lead design and ownership of a modern data platform: ingestion, Snowflake governance, pipeline reliability, migrations, data contracts, observability, and AI-enabled tooling. Partner with Analytics, Engineering, and vendors to deliver trustworthy data products and operate production pipelines.