Data Engineer Jobs in Visalia, CA

Lead product data engineering across architecture, data modeling, pipelines, monitoring, lineage, and data quality. Build reliable analytical datasets, support experimentation and machine learning use cases, and resolve business-critical pipeline issues. Partner with product and engineering stakeholders to translate business needs into automated data solutions. The role requires ownership of end-to-end data engineering projects, on-call participation, and development of AI-assisted monitoring and automation.

Lead product data engineering initiatives by designing data models, owning critical pipelines, building data quality and lineage monitoring, and supporting experimentation and ML-ready datasets. Translate business needs into automated data solutions, maintain pipeline SLAs through on-call support, and develop AI-assisted tools for issue detection and resolution. Collaborate with Product, Engineering, Data Science, Machine Learning, and Applied AI teams across Block’s products.

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.

Build and operate a large-scale enterprise data platform spanning batch and streaming processing, lakehouse storage, Kafka infrastructure, APIs, observability, and agentic MCP interfaces. Develop reusable ingestion and data-quality frameworks, improve scalability and resilience, establish engineering practices, support production operations, and mentor engineers. The role requires deep ownership of distributed data infrastructure and practical use of AI coding agents.

Owns biological knowledge representation through LinkML and Biolink ontology engineering, semantic mappings, metadata harmonization, and schema governance. The role administers agentic curation and MCP tooling, develops LLM-driven extraction and validation workflows, and establishes automated quality controls, provenance, CI/CD, and evaluation standards. This senior individual contributor translates biological research requirements into canonical data models supporting genomics, single-cell, screening, imaging, and prior-knowledge datasets.

Own and scale a multi-cloud data platform across AWS and Azure. Manage Apache Airflow, Kubernetes, Terraform, Helm, ArgoCD, CI/CD, observability, big data infrastructure, governance tooling, and FinOps initiatives. Provide end-to-end architectural and operational ownership, improve developer tooling, define platform roadmaps, manage incidents, enforce engineering standards, and mentor data platform engineers.

Design and scale lakehouse architecture, streaming and batch pipelines, and reliable data platforms using Kafka, Spark, Airflow, Iceberg, Databricks, and related technologies. Build Medallion-layer data systems, manage open table formats, optimize distributed queries, monitor platform reliability, resolve data quality issues, and collaborate with analysts, data scientists, and product teams.

Design and implement high-performance, scalable storage systems and client libraries for AI workloads. Build data loading, checkpointing, caching, POSIX-style and object-store integrations, observability and telemetry, and validate performance with platform, SRE, and AI teams using modern engineering practices.

Lead the architecture, implementation, and operation of DGX Cloud’s shared data platform. Build scalable batch and streaming pipelines, data models, APIs, libraries, and orchestration capabilities for fleet telemetry and operational data. Own cross-team technical initiatives, production investigations, data quality, observability, security, and reliability standards. Provide hands-on coding, architectural leadership, mentorship, and guidance for migrations, distributed systems, cloud infrastructure, and production data products.

Leads architecture and delivery of DGX Cloud’s shared data platform, including batch and streaming pipelines, data products, orchestration, and production infrastructure. Owns scalable data models, reliability, security, observability, and engineering standards across teams. Diagnoses complex production issues, drives migrations and performance improvements, mentors engineers, and personally develops critical software supporting fleet telemetry, capacity, utilization, cost, scheduling, and operational analytics.

Build and operate Block’s data platform, including warehousing, orchestration, business intelligence, governance, and agentic workflows. Develop reliable Python services, developer tools, and Terraform infrastructure for petabyte-scale data. Partner with data practitioners and product teams, establish engineering standards, provide technical direction, and mentor engineers while ensuring security, reliability, observability, usability, and data governance.

Build and operate a scalable data platform supporting warehousing, orchestration, business intelligence, governance, and agentic workflows. Develop Python services, Terraform infrastructure, and developer tools for petabyte-scale data. Partner with data practitioners and product teams, establish security and reliability patterns, set technical direction, improve engineering standards, and mentor engineers.

Develop technical enablement content, product demonstrations, solution guides, workshops, and training for HPE Apstra data center networking. Support sales, systems engineering, partners, and customers through presentations, technical expertise, and field enablement. Collaborate with engineering, product management, marketing, and regional teams on launches and positioning. Translate complex networking, automation, AI, and cloud infrastructure concepts into compelling technical narratives and customer value.

Prototype and integrate GPU-accelerated distributed data processing, compression, databases, and analytics solutions. Optimize complex data-intensive workloads across heterogeneous CPU/GPU architectures, collaborate on next-generation hardware and software designs, and work with customers and cloud service providers to deploy solutions and influence open standards. The role requires deep expertise in parallel programming, C/C++, accelerator architecture, memory systems, and compression or distributed data systems.

Design and optimize high-performance C++/CUDA libraries for GPU-based data processing. Improve multi-node GPU communication (UcxExchange), optimize Presto GPU query performance, and lead enhancements across Presto, Velox, and cuDF projects for efficient DataFrame and database acceleration.

Design, develop, and optimize GPU- and CPU-accelerated image and data-processing libraries. Perform performance tuning, benchmarking, and optimization across architectures; collaborate with product management and partners; provide technical leadership and guidance to library engineers; and improve library performance and maintainability.