data-pipelines jobs in San Diego

Software Engineer responsible for scoping and defining requirements, designing and implementing solutions using modern programming languages and technology stacks, and ensuring quality through secure coding practices and testing. The role involves converting customer requirements into refined visions, developing software interfaces across network protocols, integrating components into complex environments, and supporting both lab and on-site customer testing.…See more Requires 3–5+ years of relevant experience, strong understanding of SDLC, object-oriented programming, distributed systems, algorithms, and data structures. Travel up to 10% required.

SHEIN is seeking a Senior Data Engineer to build and productionize GenAI/LLM solutions for data engineering workflows, including code assistance, metadata discovery, and knowledge access. The role focuses on developing RAG and agentic workflows, automating incident triage and log analysis, and integrating AI capabilities into internal developer tools and services.…See more You'll own scoped projects from problem definition through production support, requiring 3+ years of hands-on production experience with strong Python/SQL fundamentals, proven GenAI/LLM application development, and practical knowledge of databases, APIs, data pipelines, and distributed systems.

Staff Software Engineer to architect and lead Intuit's next-generation One Logging system, designing high-scale, mission-critical logging pipelines for on-premise and public cloud (AWS, GCP) deployments. Own end-to-end platform evolution from log ingestion through routing, storage, and cost optimization, including components like FELS, Kinesis/CloudWatch integrations, and Log Router.…See more Lead modernization of legacy tooling, design edge collection agents (Fluent Bit, OIL sidecar), and drive automation initiatives using MCP Server capabilities. Provide technical leadership, mentorship, and cross-functional collaboration to align logging platform capabilities across the organization.

The System Administrator Expeditionary Systems (OpAI) will maintain and integrate AI capabilities into Naval expeditionary systems, including sensor kits, unmanned systems, C2 platforms, and TAK. Daily responsibilities span hands-on troubleshooting of network connectivity and Linux systems, updating edge servers, testing machine learning capabilities, and ensuring high-quality data flow between tactical systems.…See more The role requires 3+ years of experience in system administration, data engineering, or related fields, with intermediate Linux proficiency, familiarity with edge servers and containerization (Docker, Kubernetes), and experience with tactical data pipelines. The successful candidate will collaborate with engineers, ML specialists, and data teams to integrate cutting-edge AI solutions while supporting operational forces.

Lead the design and governance of Sharp HealthCare's enterprise data architecture, including data models, warehouses, and integration patterns supporting clinical, operational, and strategic analytics. Collaborate with IT, analytics teams, and business stakeholders to translate organizational needs into scalable, compliant data solutions.…See more Optimize data pipelines, define data standards, and guide implementation of data platforms while mentoring technical team members and contributing to long-term data strategy.

Senior Data Engineer role focused on building and productionizing GenAI/LLM solutions for data engineering workflows. You will develop RAG and agentic workflows, automate incident triage and root-cause analysis, integrate AI capabilities into internal developer tools, and own projects from problem definition through production support.…See more Required: 3+ years of production systems experience, strong Python/SQL, hands-on GenAI/LLM application building, and software engineering fundamentals including testing, CI/CD, and monitoring.

The System Administrator Expeditionary Systems (OpAI) will maintain and troubleshoot tactical systems integrating AI capabilities into Naval expeditionary platforms, including sensors, unmanned systems, and command-and-control infrastructure. Daily responsibilities span hands-on system administration (Linux, edge servers, software updates), testing machine learning capabilities in field environments, and collaborating with engineers to deploy and monitor data pipelines connecting tactical systems.…See more The role requires 3+ years in system administration, data engineering, or related technical fields, with intermediate Linux command-line proficiency, familiarity with containerization (Docker, Kubernetes), edge computing platforms (Jetson Nano, DGX), and troubleshooting of tactical data architectures. The candidate will work closely with machine learning engineers, data engineers, and software engineers to ensure performant, reliable data flow across integrated systems.

js, Python, or Go. Responsibilities include architecting relational and NoSQL databases for high availability, building data pipelines and asynchronous processing systems, and deploying cloud infrastructure on AWS with Docker, Kubernetes, and Terraform.…See more pipelines and asynchronous processing systems, and deploying cloud infrastructure on AWS with Docker, Kubernetes, and Terraform. The role requires 3–6 years of experience building distributed systems at scale, with deep expertise in database design, caching strategies, and cloud platforms. You'll also own monitoring, performance optimization, and comprehensive testing across unit, integration, and end-to-end scenarios.

Lead the product vision, strategy, and roadmap for diagnostics and debug analytics across pre-silicon and post-silicon semiconductor design phases. Own market analysis, PRD development, and technical requirements definition while serving as the subject-matter expert for DFT, silicon debug methodologies, and root-cause analysis.…See more Partner with R&D, field teams, and data science groups to build diagnostic data pipelines, anomaly-detection models, and correlation engines that accelerate SoC development and yield ramp cycles. Engage directly with customers, executives, and cross-functional teams to translate complex debugging challenges into innovative analytics solutions and drive go-to-market strategy.

Lead the design and delivery of enterprise AI platforms and applications on Google Cloud, leveraging Vertex AI, Gemini, and cloud-native technologies. Design, fine-tune, and govern LLM solutions; build RAG and agentic systems; and define end-to-end architectures spanning data pipelines, feature engineering, model lifecycle, APIs, and MLOps/LLMOps.…See more Architect cloud-native applications on GKE, Cloud Run, and managed services while implementing security, governance, and production-grade monitoring for AI/ML systems at scale.

SAIC seeks an experienced System Administrator to maintain and integrate AI capabilities into expeditionary systems for the Navy, including sensors, unmanned systems, command-and-control, and TAK. The role combines hands-on administration (Linux systems, edge servers, software updates, network troubleshooting) with collaboration on machine learning integration, data pipeline monitoring, and tactical system testing at field exercises.…See more The candidate will work across cross-functional teams—including ML engineers, data engineers, and software engineers—to deploy and validate high-performance data flows in operational environments. Requirements include 2+ years in a related technical role (sysadmin, data engineer, software engineer, or similar), intermediate Linux command-line proficiency, familiarity with containerization (Docker, Kubernetes) and edge GPUs (Jetson Nano, DGX), and experience with tactical system troubleshooting and data pipeline deployment.

Design, develop, and optimize machine learning systems for production AI platforms, including model development, inference optimization, and scalable ML infrastructure. Build training-to-deployment pipelines, optimize model serving for latency and cost, and integrate LLMs and generative AI models into microservices and APIs.…See more Develop data pipelines for ingestion, preprocessing, and feature engineering while collaborating cross-functionally with product, platform, and hardware teams to deliver end-to-end ML solutions.

Own and deliver small to medium machine learning system components from design through production deployment, including building data pipelines, training and evaluating models, and implementing MLOps monitoring. Write high-quality Python code to translate technical requirements into maintainable solutions, working with frameworks like scikit-learn, TensorFlow, PyTorch, and HuggingFace.…See more Design and deploy ML models as microservices, APIs, batch jobs, or streaming components on AWS, with responsibility for model performance metrics, data drift detection, and retraining triggers. Collaborate across Data Engineers, Software Engineers, Data Scientists, and product stakeholders to deliver project objectives.

Senior Python Backend Engineer building and maintaining Python-based APIs and services in an AWS environment. The role focuses on developing Kafka-based data pipelines, managing data movement across systems, and ensuring reliability and performance of containerized services.…See more You'll need strong production Python experience, familiarity with streaming or message-based systems, and comfort working with AWS and Docker.

Build and operate the data and ML infrastructure powering an AI platform for materials science, owning both sides: data pipelines that ingest and curate large-scale scientific output into training-ready formats, and model packaging, serving, monitoring, and CI/CD systems that move models safely from research to production across customer environments. You will design data ingestion and transformation workflows, implement validation and quality gates, package and version models with reproducible builds, run models through batch and online inference with safe rollout and rollback, monitor for drift and degradation, and build observability and internal tooling for engineering and science teams.…See more The role requires 6+ years shipping production software with deep expertise in data systems, ML infrastructure, containers, orchestration, and observability.

As Principal Software Engineer for AI & Data Platform, you will architect the data foundation for scientific and engineering R&D platforms, designing scalable data processing patterns, ML training pipelines, and intelligent workflow interfaces. You will own end-to-end responsibilities including data modeling for multi-use analytics and ML, building production training and fine-tuning pipelines, model evaluation and benchmarking, and setting engineering standards for the team.…See more The role requires 10+ years shipping production software, expert-level Python, deep experience with large-scale data systems (object storage, analytical processing, training formats), hands-on ML pipeline development, and the ability to set technical direction in early-stage environments while implementing it yourself.

Lead day-to-day technical project delivery for Distribution Operations initiatives including outage management, solar/battery data integrations, forecasting, automation, and analytics. Transform technical requirements into delivery plans, sprint backlogs, and release milestones while coordinating work across data engineers, architects, cybersecurity teams, and vendors.…See more Manage project controls, scheduling, defect tracking, and comprehensive documentation across integrations, ETL/ELT pipelines, cloud analytics, and Power Platform solutions. Facilitate design reviews, support operational readiness, and ensure technical solutions meet engineering rigor and real-world operational needs.

Lead the architectural design of server-side, backend, and cloud platform capabilities supporting Dexcom's continuous glucose monitoring products. Collaborate across product, cybersecurity, firmware, data, and mobile teams to translate business and regulatory requirements into secure, scalable, and maintainable platform solutions.…See more Establish architectural direction for distributed services, APIs, data pipelines, event-driven systems, and shared platform capabilities. Guide technology exploration, system integration, performance testing, and serve as an authority for platform architecture principles and modern backend development practices across the organization.

As a client-facing data analyst, you'll work with Department of the Navy clients to understand their data needs, transform complex structured and unstructured data into actionable insights, and advise on how findings impact DoD acquisition programs and IT modernization. You'll use tools like Tableau, Python, R, or SQL for data manipulation; Databricks or Spark for data engineering; and Power Apps/Power Automate for automation.…See more You must hold or be able to obtain a Secret clearance and have 4+ years of DoD client experience.

Lead the entire engineering team at a VC-backed AI trading firm, combining hands-on ML research and model development with technical leadership. Own the end-to-end development of production AI systems deployed in live financial markets, including model design, training, evaluation, and infrastructure.…See more Set technical direction across ML and software engineers, drive hiring and team development, and translate research into measurable real-world trading performance. Require 5–8 years of applied ML/AI experience with 3+ years managing full engineering teams.

Design, develop, and deploy end-to-end generative AI solutions including GenAI agents, LLMs, and applications to solve complex business challenges. Build data pipelines, design APIs for AI accessibility, and collaborate with cross-functional teams (developers, data scientists, business) to implement enterprise GenAI solutions.…See more Requires 4+ years of full-stack application development (Java, Python, JavaScript) and 3+ years with data structures, algorithms, and data stores. Stay current with AI research and communicate technical concepts to non-technical stakeholders.