<p>Kforce has a client in Salt Lake City, UT that is seeking a Staff Data Engineer.</p> <p>Summary: Our partner's Enterprise Technology & Operations team is driving a major modernization effort and seeking a highly experienced Staff Data Engineer to lead the end-to-end migration of on-premise data warehouse systems to modern cloud data platforms (AWS, GCP, or Azure). This role is highly technical, hands-on (80% coding), and central to shaping the future state of our partner's data ecosystem.</p> <p>Key Responsibilities:</p> <ul> <li>Lead technical strategy, architectural design, and roadmap development for cloud-based data platforms </li><li>Design, build, and optimize scalable data pipelines, workflows, and ELT/ETL processes (Databricks Workflows, Kafka, modern ELT tooling) </li><li>Develop distributed, production-grade solutions using Python, PySpark, and advanced performance-tuning techniques (partitioning, caching, parallelism, efficient data formats) </li><li>Architect and manage cloud-optimized data infrastructure using Databricks on GCP, including cost-efficient Spark workload strategies (right-sizing, autoscaling, serverless, job compute, cluster pools, spot/preemptible workers) </li><li>Build and manage CI/CD pipelines supporting automated testing and deployments </li><li>Design distributed data platforms (data lakes, data warehouses) with strong OLTP/OLAP modeling principles for both real-time and analytical workloads </li><li>Ensure secure data movement and integration between cloud and on-prem environments </li><li>Drive data governance, compliance, and security best practices </li><li>Mentor engineers, lead technical discussions, and coordinate cross-functional efforts </li><li>Produce clear technical documentation and contribute to engineering community initiatives* 10+ years of hands-on data engineering experience; bachelor's or equivalent combination of experience and education </li><li>Significant experience leading full lifecycle migrations from on-prem data warehouses to cloud platforms </li><li>Deep expertise with distributed systems, Python, PySpark, and scalable data architectures </li><li>Strong experience with cloud platforms (GCP preferred), Kubernetes, Docker, and IaC </li><li>Proven success designing cost-optimized Databricks implementations and medallion-style architectures </li><li>Extensive experience supporting both cloud and on-prem environments with secure file/data movement </li><li>Strong communication skills, leadership ability, and experience mentoring engineering teams </li><li>Ability to develop thorough and accurate technical documentation </li></ul>