Data Engineer Jobs in West Jordan, UT

Build and maintain scalable fraud data pipelines, Snowflake data marts, dbt models, and summarized metric tables. Partner with fraud analysts and engineering teams to enable reporting, scorecards, monitoring, and operational decision-making. Establish data engineering standards, documentation, testing, governance, data quality, and pipeline reliability for audit-ready fraud analytics infrastructure.

Build and operate scalable batch and streaming data pipelines on AWS, lead event data architecture, implement data quality and observability, and develop dimensional models for analytics. Create feature pipelines and feature-store infrastructure supporting personalization, recommendation, and machine-learning systems. Partner cross-functionally on ML workflows, AI product instrumentation, warehouse optimization, engineering standards, and technical mentorship.

Build and integrate deep learning models into software pipelines, collaborate with product and engineering teams, support data ETLs, and deploy solutions to cloud-hosted environments.

Lead data engineering projects by designing scalable data solutions, pipelines, ETL processes, cloud-native applications, and ML-enabled analytical tools. Responsibilities include architecture design, code reviews, CI/CD, documentation, mentoring engineers, client engagement, proposal support, and collaboration with consulting teams. The role requires strong Python, SQL, cloud, big-data, orchestration, and DevOps expertise, with international client travel as needed.

Lead data engineering initiatives involving large-scale data solutions, cloud-native applications, ETL pipelines, data architectures, CI/CD, and machine learning deployment. The role includes mentoring engineers, ensuring code quality, documenting operational processes, supporting consulting teams, engaging clients, contributing to proposals, and delivering analytics solutions globally. International client travel may be required.

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, develop, and monitor large-scale cloud-native data solutions and ETL pipelines; support ML model deployment, ensure code hygiene and CI/CD, mentor engineers, produce documentation and runbooks, and engage with clients to deliver analytics-driven solutions.

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.