Data Engineer Jobs in Visalia, CA

Design and maintain data architecture and pipelines to support compliance and risk teams. Build and optimize data models, standardize metrics, and create data dictionaries. Implement data quality, lineage monitoring, AI-driven agents for false-positive reduction and automation, and participate in on-call rotations to ensure SLAs are met.

Lead design and optimization of data models and pipelines for compliance and risk; standardize metrics and documentation; build data quality, lineage, and monitoring (including AI agents for automation); manage ETL scheduling, on-call pipeline support, and collaborate with product and non-technical partners to translate business needs into automated, production-ready data solutions.

Design, build, and operate production ML signal systems—ranking, retrieval, recommendations, propensity, and next-best-action—covering feature/candidate generation, serving, experimentation, monitoring, and feedback. Define signal contracts (freshness, provenance, confidence), evaluate long-term impact (trust, fairness, compliance), and partner across product, data, and risk teams to deliver reusable customer-intelligence capabilities.

Represent Operations in huddles and sprints, simplify and automate processes, design target solutions and roadmaps, implement automation using Python/OCR/AI, track work in JIRA, and coordinate small-to-medium programs while managing onshore/offshore teams to drive operational improvements.

The role involves designing and optimizing backend systems for data migration and integration tools. Collaborates with teams to enhance performance and scalability.

As a Senior Developer Advocate, you will lead community engagement in Data Observability, create content, mentor teams, and enhance product feedback from engineers.

Design, build, and operate production ML systems that generate trusted signals for ranking, retrieval, recommendations, propensity/churn/LTV, and next-best-action decisioning. Define signal/data contracts, own feature and candidate generation through serving, experimentation, monitoring, and feedback loops, and evaluate long-term business impact, trust, fairness, and compliance. Partner across product, data, modeling, risk, and compliance and apply AI/agents to accelerate engineering and operations.

Partner with sales to design and present data security solutions, lead technical evaluations and custom configurations, gather customer requirements, and drive successful hands-on proof-of-concept and evaluation outcomes for the Data Security business line.

Design and implement high-performance, parallel algorithms in C++ and CUDA to accelerate dataframe and database operations. Contribute to open-source projects (cuDF, Velox, Presto, Spark), profile and optimize code, and collaborate with GPU hardware and software experts to build scalable data-processing libraries.

The Principal Data Platform Engineer leads the architecture and development of the enterprise data platform, focusing on analytics, data science, and AI/ML capabilities. They establish standards for data quality and observability, design data ingestion and processing patterns, and mentor team members to enhance technical capabilities.

Design and build cloud-based data applications and pipelines to analyze clinical data, enrich and provision datasets, and support clinical/operational processes. Mentor junior engineers, drive R&D for repeatable templates, collaborate with Product/Platform/Architecture, and prioritize source control, documentation, and simple solutions using modern data, ETL, and LLM tooling.

The Data Integration Engineer will manage data integrations and ETL processes for healthcare data, ensuring accuracy and accessibility for business users.

Design and build scalable, semantically consistent 360 entity data models and transformation pipelines. Implement cleansing, enrichment, KPIs, scoring, and business rules in SQL and Python/Scala, ensure data quality, lineage, and performance at scale. Use AI coding assistants and SDD to accelerate development, create tests and documentation, and collaborate with domain experts, data scientists, and product teams to deliver trusted, reusable data assets.