Top Data Engineer Jobs in Provo, UT

Designs and builds scalable batch and real-time data pipelines, ingestion workflows, analytical systems, data products, and intelligent APIs. Ensures data quality, reliability, and performance while supporting cloud-based data architecture for BI and AI/ML use cases. Collaborates with cross-functional teams, participates in code reviews, and follows DevOps and CI/CD practices using Databricks, Spark, Python, SQL, and Azure services.

Build and maintain FinOps data pipelines, BigQuery and dbt models, cost attribution systems, forecasting and anomaly detection models, and cost-versus-revenue analytics. Identify and implement AWS and GCP savings opportunities, automate governance with Python, Terraform, and Lambda, and partner with Engineering, Finance, Product, and leadership on cloud costs, forecasting, reporting, and architecture decisions.

Designs, develops, maintains, and improves software applications and business processes. Responsibilities include full lifecycle development, troubleshooting, performance and reliability improvements, CI/CD collaboration, requirements analysis, technology research, package maintenance, portfolio planning, and mentoring junior engineers. The role requires independent work, cross-functional collaboration, and strong software development, documentation, and problem-solving skills.

Build and maintain scalable Python-based data pipelines and cloud-native solutions supporting U.S. Army financial-system modernization. Develop ETL/ELT workflows using AWS Glue, Lambda, S3, and CloudWatch; integrate APIs and diverse file formats; improve data reliability, performance, security, and governance; implement testing and monitoring; and collaborate with engineers and stakeholders in an Agile environment.

Designs, develops, maintains, and optimizes complex data systems, SQL/ETL processes, reporting solutions, and .NET applications supporting pharmacy benefit management. Responsibilities include database performance tuning, data warehousing, troubleshooting pharmacy claims, technical specification development, software testing, cloud integration, stakeholder collaboration, Agile participation, and mentoring. The role requires strong SQL Server, dimensional modeling, cloud database, Python, PowerShell, and healthcare data standards expertise.

Designs and optimizes scalable data platforms, architectures, and production pipelines supporting AI and machine learning. Responsibilities include Databricks Lakehouse development, batch and real-time processing, data governance, security, monitoring, performance tuning, infrastructure planning, and collaboration with DevOps, MLOps, and cross-functional teams. The role serves as the AI team’s data engineering subject matter expert and ensures reliable, secure, cost-effective data solutions.

Develop and maintain data pipelines, infrastructure, and integrations supporting analytics, data science, and business intelligence. Ensure data quality and integrity, optimize workflows, and collaborate with Operations, IT, Accounting, HR, Property Services, Marketing, and other departments. The role requires SQL Server, programming, shell scripting, cloud, containerization, version control, and data pipeline knowledge, with occasional travel and flexible scheduling as project deadlines require.

Leads the design, development, and optimization of scalable data pipelines and analytics platforms. Builds ETL/ELT workflows using Databricks and Spark, integrates and transforms source data into curated models, manages SQL Server data, and maintains documentation and lineage. Collaborates with product, analytics, and engineering teams to deliver reliable data solutions. Applies responsible AI practices and delivers projects independently within established timelines.

Design, build, and operate end-to-end data pipelines and models for healthcare claims and payer data. Responsibilities include ingestion, canonical and analytical data modeling, legacy process migration, data quality, testing, CI/CD, deployment, operations, incident response, tool integration, documentation, and onboarding new data sources. The role partners across ML, product, analytics, platform, operations, and customer teams while critically evaluating AI-generated engineering work.

Build and operate scalable batch and streaming data pipelines, dimensional models, ELT/ETL workflows, and trusted data products. Improve data quality, observability, reliability, performance, security, and cost efficiency across the cloud data platform. Partner with analytics, risk, finance, product, data science, and machine learning teams to support business and AI use cases. Lead technical initiatives, influence architecture, mentor engineers, and establish engineering best practices.

Own Mux’s end-to-end data platform, including ingestion, Snowflake administration, dbt transformations, orchestration, data quality, observability, and reverse ETL. Build semantic models supporting AI agents, establish governance and reliability standards, and partner with Engineering, Finance, Product, and GTM. Use AI coding tools to develop and maintain pipelines while validating output quality. The role requires strong SQL, Python, data modeling, cloud warehouse administration, and production data engineering experience.

Own end-to-end customer data migrations from legacy databases and files into GovPilot. Use AI coding agents to profile data, create mappings, generate transformation and loading scripts, automate validation, and build reusable migration pipelines. Review and correct AI-generated SQL and scripts, identify data quality issues, maintain auditable migration logic, manage multiple client migrations, and support evening or weekend production go-lives.

Designs, builds, and optimizes scalable Databricks-based data platforms, pipelines, integrations, and reusable engineering tools. The role focuses on platform performance, reliability, cost efficiency, security, governance, automation, and cloud infrastructure. It collaborates with engineering, analytics, security, and other stakeholders on architecture, technical planning, production support, and continuous improvement. The position requires hands-on data engineering experience with Databricks, cloud services, Python, SQL, Spark, infrastructure-as-code, and CI/CD practices.

Build and operate a governed Snowflake enterprise data warehouse, including ingestion, orchestration, dbt transformations, infrastructure as code, security controls, reliability checks, and CI/CD. Partner with data owners and BI teams during migration from legacy systems, ensure compliance for sensitive financial data, support reconciliation and incident resolution, and mentor engineers. The role requires hands-on ownership of pipelines and platform components across development and production environments.

Plans and executes enterprise data migrations, conversions, transformations, archival, and cloud data platform operations. Builds ETL/ELT pipelines and migration scripts, performs profiling and source-to-target mapping, validates data, manages cutovers and rollback procedures, and troubleshoots migration issues. Optimizes cloud platforms, databases, workloads, security, access, cost, and performance while collaborating with engineering, governance, architecture, and business teams.

Lead the architecture and development of Penn Foster Group’s Databricks-based cloud data platform. Build scalable batch, streaming, CDC, and API pipelines using Python, SQL, Spark, and Azure; establish engineering standards, governance, observability, and CI/CD practices; develop AI-ready semantic data products; and mentor data engineers. Partner with BI, product, security, governance, and MLOps teams to support analytics, machine learning, generative AI, and self-service reporting.

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 production of marine and anadromous species range maps under FWS standards. Aggregate, normalize, transform, and validate spatial data; analyze habitat intersections; coordinate review cycles with FWS and NOAA Fisheries subject matter experts; document quality assurance; and obtain government sign-off for deliverables.

Designs and leads enterprise data pipelines and integrations across Snowflake, Oracle Fusion, on-premises databases, and cloud applications. Ensures data quality, governance, security, scalability, and performance while supporting Power BI, AI/ML, and self-service analytics. Partners with business and technical stakeholders, leads migrations, mentors engineers, optimizes data platforms, and establishes engineering best practices.

Designs, builds, deploys, and maintains scalable data pipelines, data lake systems, batch and streaming processing, MLOps frameworks, data documentation tooling, and an agentic data exploration platform. The role supports customer-facing products and enables engineering teams through reliable infrastructure, observability, data discovery, and AI capabilities.

Build and operate scalable batch and streaming data pipelines, data lake infrastructure, MLOps frameworks, data documentation systems, and an AI-assisted data exploration tool. The role focuses on reliable data ingestion, large-scale processing, workflow orchestration, observability, and infrastructure supporting customer-facing products and machine learning development.

Build and maintain production data ingestion, transformation, and pipeline systems for a governed federal data and AI platform. Develop data products, infrastructure-as-code, CI/CD workflows, monitoring, quality controls, cataloging, and access controls within FedRAMP, NIST 800-171, and CUI constraints. Support data science and machine learning teams, troubleshoot reliability and performance issues, and document engineering processes. The role is fully remote and requires U.S. citizenship and the ability to obtain a T5/SSBI adjudicated clearance.

Build and maintain production data pipelines that transform robot logs and telemetry into reliable, queryable datasets. Design Spark-based ETL workflows, orchestrate processing with Airflow or similar tools, manage gold-layer datasets, and implement data quality, schema evolution, monitoring, and metadata practices. Collaborate with robotics, hardware, reliability, analytics, and ML teams on data modeling, fleet insights, debugging, and root-cause analysis. The role may also contribute to Python, Java, or C++ libraries supporting data capture and consumption.

Designs and leads production-grade batch, incremental, streaming, and real-time data pipelines across Microsoft Fabric and Azure. Builds Delta Lake medallion architectures, dimensional and Data Vault models, CDC and incremental-loading solutions, CI/CD automation, governance, security, observability, and data-quality controls. Supports migrations to Fabric, prepares governed data for AI and RAG workloads, and leads client-facing engineering workstreams, estimates, risk management, mentoring, and operational handover.

Own PushPress’s data platform, including ingestion, orchestration, cloud warehouse performance, governance, privacy, data quality, monitoring, and AI-ready data foundations. Build reliable pipelines integrating product, payment, and third-party data; establish data contracts, access controls, auditability, and semantic definitions; support real-time analytics and trustworthy reporting. Partner with Engineering, Data, and AI teams while maintaining infrastructure, incident response, documentation, and operational runbooks.