etl 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.

Senior hands-on software engineer building the core orchestration, data systems, and internal tooling for an automated chemistry platform. You'll own problems end-to-end across the full stack—Python backend, TypeScript/React frontend, databases, APIs, and distributed systems—designing services, modeling data flows, and building interfaces that connect synthesis requests from intake through execution.…See more You'll work closely with automation engineers, chemists, and ML teams to ship production features, establish engineering practices, and become a primary owner of major platform components. The role prioritizes judgment and problem-solving over language expertise, expecting fluency across the stack and comfort reaching for languages like Rust, Go, or C++ when warranted.

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.

Design, build, and maintain data pipelines that ingest, transform, and load data from SQL Server, PostgreSQL, SaaS platforms like HubSpot, and internal applications into Snowflake. Develop and optimize data models, manage data quality and governance, and create dashboards and reports to support reporting and analytics use cases.…See more Collaborate with business stakeholders to gather requirements and translate them into scalable data solutions. Optimize query performance and storage efficiency within Snowflake while implementing best practices in documentation and pipeline monitoring.

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.

The Data Systems Specialist manages inbound order and donor data, develops and maintains SQL Server and ETL processes for automated import and reconciliation workflows, and ensures data accuracy across reporting systems. Core responsibilities include scrubbing and validating orders from multiple sources, developing SSIS packages for data transformation, supporting autopay and EFT processing, and creating Power BI reports and semantic models for internal departments.…See more The role requires strong proficiency in SQL Server, T-SQL, SSIS, and Power BI, with the ability to follow complex business logic and validate data accuracy independently. Success depends on collaborating with Customer Service, Finance, and Media teams to gather requirements and resolve data processing issues.

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.

S. Navy environment.…See more t, deploy, and maintain robust Oracle database solutions supporting a U.S. Navy environment. Develop and maintain Oracle database objects (tables, indexes, views, procedures, packages, triggers) using SQL and PL/SQL, integrate databases with web applications using JSON, troubleshoot database issues, and apply secure development practices throughout the full software development lifecycle. Requires minimum 3 years of database programming experience with Oracle 12c or greater, strong SQL and PL/SQL expertise, and knowledge of the complete SDLC from planning through maintenance.

Staff-level data engineer who designs, develops, and maintains scalable ETL/ELT pipelines using Databricks, PySpark, and Python for enterprise analytics and AI use cases. Builds curated data layers following Lakehouse Medallion architecture, develops Databricks-native applications (notebooks, dashboards, APIs), and optimizes Apache Spark workloads for performance and cost efficiency.…See more Owns production data pipelines end-to-end, ensures data quality and reliability, and acts as a technical leader mentoring teams on data engineering best practices and modern Databricks capabilities.

The Product Owner will manage the product backlog and roadmap for a data platform serving alternative investment firms, applying deep expertise in ETL/ELT pipelines, data warehousing, and self-service reporting. The role requires translating complex client and business requirements into technically feasible product solutions while collaborating closely with engineering on Snowflake-based architectures, BI tools, and API integrations.…See more Success depends on defining metrics, monitoring post-release performance, driving adoption, and continuously refining the product based on customer feedback and usage data. The ideal candidate has 5+ years in product ownership or product management within fintech/alternative investment software, with strong domain knowledge of fund accounting, portfolio reporting, and investor reporting workflows.

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.

Senior/Staff Data Engineer responsible for building production data pipelines, APIs, and BI tools that serve Agoda's contact center operations. Will design and maintain dashboards, metrics, and analytics platforms using SQL, Python, Java, Golang, or TypeScript; work with data warehouses like BigQuery, Snowflake, or Redshift; and orchestrate ETL/ELT workflows using tools such as Airflow or dbt.…See more At Staff level, defines roadmap for key data platforms, mentors junior engineers, and influences cross-team data architecture to scale with business growth.

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.

The AI Architect II is a senior technical leadership role responsible for establishing architectural standards and patterns for AI solutions, including RAG pipelines, agentic workflows, and model serving. The role involves designing scalable AWS cloud architectures, defining MLOps practices, implementing responsible AI governance, and translating business requirements into enterprise AI and cloud strategies.…See more The position requires hands-on technical expertise to evaluate and integrate AI/ML services, lead systems design across microservices and event-driven architectures, and mentor engineering teams on cloud and AI best practices. This role drives technology decisions with real business impact while modernizing the company's platform through cloud-native and AI transformation initiatives.

Lead a team of cloud engineers building the nervous system for Brain Corp's fleet of 30,000+ autonomous mobile robots. Own the platform's reliability, roadmap, and architecture while managing team hiring, onboarding, and career development.…See more Drive high-performance delivery across robot and customer-facing interfaces, balance competing priorities (performance, cost, reliability, scalability), and partner with principal and staff engineers on long-term platform strategy. Stay hands-on enough to make sound architectural decisions and earn senior engineer trust during a period of significant growth.

Lead a team of cloud engineers building the backend platform that powers Brain Corp's global fleet of 30,000+ autonomous mobile robots. Own the platform's reliability, roadmap, and architecture as it scales to support fleet operations and customer-facing applications.…See more Balance hands-on technical leadership with people management—mentor engineers, drive hiring and onboarding, own production reliability and incident response, and partner with principal/staff engineers on distributed-systems architecture. Navigate tradeoffs between performance, cost, reliability, and scalability while shipping features that robots in the field depend on.

Design, build, and maintain data pipelines that ingest, transform, and load data from SQL Server, PostgreSQL, SaaS platforms (HubSpot), and internal applications into Snowflake. Develop and optimize data models, manage data ingestion using Azure Data Factory and data replication tools, and ensure data quality and integrity.…See more Collaborate with business stakeholders to translate analytics requirements into scalable solutions, develop dashboards and reports, and optimize query performance. Implement data governance best practices, monitor pipelines, and recommend technology improvements to support the company's data strategy.

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.

Sr. Director role overseeing SIE's data and AI platform ecosystem, including data infrastructure (streaming, ingestion, warehouse), experimentation platforms, and governance tools.…See more Responsible for defining long-term vision, leading global engineering teams, and ensuring platforms process massive data volumes to deliver insights across gaming, content creation, and enterprise operations. Must drive scalability, cost efficiency, security, and operational excellence while collaborating with senior leadership to align platform strategy with business objectives. Requires proven Sr. Director-level experience and thought leadership in platform architecture, real-time data processing, and ML/AI integration.

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.

This is a hands-on security engineering role focused on running Saronic's cyber threat intelligence program. The engineer will track nation-state and advanced criminal adversaries targeting the defense industrial base, operationalize indicators and TTPs into detections and hunts, produce finished intelligence products, and fuse external threat feeds with internal telemetry to prioritize real exposures.…See more The role requires 4+ years in cyber threat intelligence, threat hunting, detection engineering, or intrusion analysis with demonstrable experience tracking sophisticated or state-sponsored adversaries.

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.