<p>Kforce has a client in Lehi, UT that is looking for a Senior Data Engineer. This role focuses on building scalable pipelines and ensuring high-quality data for time-series modeling and predictive insights.</p> <p>Key Responsibilities:</p> <ul> <li>Design, develop, and optimize ETL processes and data pipelines for large-scale time-series datasets (e.g., energy consumption, HVAC usage) </li><li>Implement data models and storage solutions for structured and unstructured data, leveraging cloud platforms (AWS, GCP, or Azure) </li><li>Collaborate with Data Scientists to enable efficient model training and deployment for forecasting and anomaly detection </li><li>Ensure data integrity, governance, and security across all systems </li><li>Work with IoT and smart home data streams to support real-time analytics </li><li>Build and maintain data pipelines for AI/ML applications, especially for training and inference* 5+ years of data engineering or backend data systems experience </li><li>Strong proficiency in Python, SQL, and distributed data frameworks (PySpark, Databricks) </li><li>Hands-on experience with GCP, Kafka/Pub-Sub, and data lake architecture </li><li>Ability to read and modernize legacy Scala/Akka codebases </li><li>Proven track record building production-grade pipelines that deliver analytics-ready datasets </li><li>Strong problem-solving skills in ambiguous, under-documented environments </li></ul> <p>Preferred Qualifications:</p> <ul> <li>Python, SQL, cloud experience (GCP preferred), Databricks nice-to-have </li><li>Preference for engineers with production ML/AI pipeline experience, not limited to energy sector </li><li>Experience with ML platforms and feature engineering workflows (e.g. Vertex AI) </li><li>AI/ML application experience (LLMs, computer vision, energy forecasting models) </li><li>Background in IoT applications, protocols, and telemetry </li><li>Familiarity with specialized databases: Graph databases (e.g. Neo4j); Vector databases (e.g. Pinecone) </li><li>Experience with data orchestration tools (e.g. Airflow) </li><li>Background in Demand Response or home energy automation </li><li>Experience implementing data quality metrics, observability, and alerting </li><li>Track record of significant cost optimization or performance improvements in data systems </li></ul>