Data Engineer

• Own the architecture of our data platform, spanning ingestion (Hevo, Estuary), our Google BigQuery warehouse, and the dbt transformation layer — designing systems that are scalable, reliable, secure, and built to last.
• Set the technical bar as a senior IC, leading through design reviews, code review, and mentorship — raising the quality, rigor, and engineering discipline of the team without a formal management role.
• Build and harden data ingestion at scale, designing batch and CDC pipelines across a diverse set of sources — operational databases, SaaS applications, and ERP systems — and driving vendor and tooling decisions with a clear point of view.
• Develop end-to-end with AI coding agents. Our teams build with Claude Code and similar tools, and you'll help define the practices, guardrails, and standards that make AI-assisted engineering fast, safe, and high-quality.
• Invest in reliability and observability, establishing testing, monitoring, alerting, and incident-response practices so data lands on time, is trusted, and failures are caught before stakeholders notice.
• Strengthen governance and controls, implementing the access models, auditability, and operational rigor expected of a company operating at scale and under growing scrutiny.
• Lay the foundations for agentic analytics, building the well-modeled, well-governed platform that agents and MCP connections rely on to deliver trustworthy answers from data.
• Partner with analytics engineers, analysts, and stakeholders to understand data needs, unblock high-value use cases, and translate business requirements into durable platform capabilities.
• Engineer tooling and services beyond the warehouse - building the APIs, automated jobs, and internal tools in Python that make the platform run smoothly and put data in more people's hands than SQL alone can reach.
• Drive the evolution of our warehouse architecture - leading hands-on POCs on lakehouse patterns, open table formats, and alternative query engines, and deciding what's worth adopting next.

Staff Data Engineer

• Lead and grow a team of data engineers, providing mentorship and leadership within the function
• Build the future of the data engineering practice to deliver value to internal and external stakeholders while leveraging cutting edge technologies to dramatically accelerate quality, scale, and depth of delivery
• Design and implement scalable data pipelines to collect, process, and store large volumes of critical data from various sources
• Drive the evolution of the Business Data platform by leveraging modern data technologies like Spark, Airflow, and Databricks
• Collaborate across organizational boundaries with Product, Marketing, and Sales to convert business needs into technical requirements
• Authoritatively translate business use cases into well-thought-out data models that evolve with the business

Director, Data Engineering – Business Data

• Define current-state and future-state data architecture across enterprise platforms and applications.
• Design scalable data foundations across cloud and hybrid environments.
• Establish standards across data ingestion, storage, transformation, modeling, metadata, lineage, quality, and governance.
• Create architecture patterns for batch, streaming, event-driven, and API-based data integration.
• Develop conceptual, logical, and physical data models aligned with business and technical requirements.
• Assess existing data platforms and identify gaps across scalability, quality, governance, integration, security, and accessibility.
• Partner with engineering, product, analytics, security, and business teams to ensure enterprise data is trusted, usable, and properly governed.
• Support data architectures that enable analytics, generative AI, retrieval-augmented generation, and other emerging technology use cases.
• Evaluate data platforms, tools, and technologies based on scalability, security, maintainability, and business needs.
• Translate complex technical concepts into clear recommendations, roadmaps, and architecture documentation for technical and executive stakeholders.

Data Architect

• Leads the architecture, engineering, and ongoing operation of the security data lake and associated data products supporting multiple security technologies and data sources
• Establishes the technical roadmap, engineering standards, reusable design patterns, and platform operating model
• Acts as a subject matter expert among peers, with manager and senior management on all matters pertaining to data platforms and the supporting architecture
• Develop and maintain the end-to-end architecture of the security data lake, including ingestion, storage, transformation, enrichment, semantic, and consumption layers
• Ensure data pipelines comply with security policies and regulatory requirements
• Develop testing plans to ensure quality of implementation
• Provide trouble resolution on complex problems and lead implementations for data pipelines, Microsoft Fabric, Azure DevOps, REST API, and source-system integration issues
• Maintain relationship with key vendors
• Create presentations and seek IT management approval and acceptance of significant replacements or reconfigurations of the data pipeline

Senior Data Engineer

• Own data architecture end-to-end. Define how we capture, model, and serve critical business data then implement it in production. You’ll make architectural decisions around storage formats, compute patterns, and SLAs that balance cost, scalability, and consistency.
• Build mission-critical pipelines. Develop and operate streaming and batch data workflows that process high-volume events across multiple domains user activity, transactions, experimentation, marketing performance, and operational telemetry with tight guarantees for latency, completeness, and accuracy.
• Design and implement canonical models. Create domain-oriented data models that serve as the source of truth for analytics, ML, and real-time applications. Establish and enforce modeling standards, ownership boundaries, and data contracts across teams.
• Enforce data quality at scale. Build tests, lineage, monitoring, and reconciliation systems that make every dataset observable and every anomaly actionable.
• Automate operational workflows. Partner with business systems and platform teams to eliminate manual data handoffs and reconcile data across services, warehouses, and external systems.
• Enable insights and experimentation. Support analytics, ML, and product engineering teams by exposing high-quality, low-latency data through semantic layers, APIs, and real-time query systems.

Data Engineer, Payments

• Lead the end-to-end design, development, testing, and deployment of highly complex and scalable software solutions.
• Define and enforce software architecture principles, coding standards, and technical best practices across teams.
• Serve as a technical authority and provide guidance on complex system design, integrations, and platform-level decisions.
• Collaborate with product management and business stakeholders to shape technical strategy and translate requirements into solutions.
• Mentor, coach, and provide technical leadership to engineers at all levels, fostering growth and skill development.
• Conduct deep technical reviews of designs and implementations to ensure quality, performance, and maintainability.
• Identify, evaluate, and implement new tools, frameworks, and technologies to improve efficiency and system capability.
• Troubleshoot and resolve the most challenging technical issues across multiple domains and systems.
• Contribute to roadmap planning and long-term platform evolution strategies.
• Promote a culture of engineering excellence, innovation, and continuous improvement.

Staff Software Development Engineer – MCED, Lab, Data Technology

• Design, implement, validate, optimize, and deploy algorithms that turn AFM, image, sensor, and metrology data into robust measurements and insights.
• Develop production algorithm and application software using modern C++/STL, C#, .NET, WPF, and Python where appropriate.
• Apply signal processing, image processing, numerical methods, statistical analysis, pattern recognition, and machine learning to challenging real-world measurement problems.
• Translate research concepts and prototypes into maintainable, testable, high-performance product software with clear requirements and measurable performance.
• Build validation strategies using representative datasets, quantitative metrics, uncertainty analysis, and regression tests; clearly communicate algorithm limits and tradeoffs.
• Integrate algorithms into multithreaded Windows applications and instrument workflows spanning data acquisition, hardware control, processing, persistence, visualization, and automation.
• Own architecture for new analysis capabilities and guide their integration into a mature product codebase through sound interfaces, layering, refactoring, and incremental delivery.
• Lead technical investigations and collaborate with applications scientists, systems, hardware, test, and software engineers throughout the product development cycle.
• Remain deeply hands-on while providing technical direction and raising algorithm and software engineering quality across a small cross-functional team.

Software Development Engineer IV – Algorithms, Scientific Data

• Applies data science and machine learning to the analysis of electrical, vibration, and acoustic signals, transforming raw time-series sensor data into actionable diagnostics and predictive insights for rotating industrial equipment.
• Partners with engineering and domain experts to design and deploy production-grade signal processing and ML solutions for predictive maintenance across industrial applications.
• Operates effectively in ambiguous problem spaces where signal quality, environmental noise, and domain constraints require both technical rigor and adaptive thinking.
• Design and develop signal processing pipelines and machine learning models that operate on electrical (current/voltage), vibration, and acoustic time-series sensor data, including symmetrical component analysis, matched filtering, wavelet decomposition, and time-frequency analysis techniques.
• Evaluate algorithm performance using both objective metrics and subjective measures, including integration with speech recognition engines where applicable.
• Perform exploratory data analysis, feature engineering, and signal feature extraction on raw electrical, vibration, and acoustic data to surface fault patterns and anomalies.
• Analyze and interpret signals from electrical asset monitoring systems (motors, generators, pumps) utilizing electrical signature analysis, vibration analysis, and signal processing expertise to support fault isolation and anomaly detection.
• Use cross-sensor asset monitoring data (temperature, speed, load) to characterize and validate signal-derived diagnostics.
• Apply data-driven signal processing methods to characterize and isolate faults at the subsystem, component, and machine level, identifying root causes from spectral, electrical, and vibration sensor data in rotating industrial equipment.
• Contribute to end-to-end ML workflows including data ingestion, model training, inference, and monitoring for drift and degradation in live environments.
• Collaborate with engineering, product, and domain SMEs to translate operational challenges into well-scoped data science solutions.
• Communicate findings, model performance, and business value clearly through visualizations, written documentation, and presentations to technical and non-technical stakeholders.
• Explore and evaluate emerging signal processing and AI techniques, recommending production incorporation where appropriate.

Data Scientist – Signal Processing Engineer, Acoustics

• Design, implement, and oversee the enterprise data governance strategy
• Work in close partnership with the Ontology/Semantic Librarian and Data Engineering teams
• Lead the development and enforcement of data governance policies, standards, and best practices
• Collaborate with Data Engineering teams to implement governance in federated querying, GraphQL interfaces, vector databases, and RAG pipelines
• Define and maintain enterprise data stewardship processes
• Establish governance frameworks for data security, privacy, and compliance
• Design and implement data quality, metadata, and lineage strategies
• Contribute to the internal Data Marketplace by defining governance requirements for semantic data products
• Drive data governance training, change management, and adoption across business, engineering, and AI teams
• Monitor and report on governance metrics, risks, and compliance posture for the Data Unification program

Lead Data Engineer – Governance

• Lead the design, development, and governance of enterprise ontologies, semantic layers, and knowledge graphs.
• Combine deep semantic modeling expertise with hands-on implementation of graph technologies, vector databases, and federated data architectures.
• Design and build enterprise ontologies and semantic models that align business objectives with technical implementation.
• Lead the creation and maintenance of semantic layer graphs, knowledge graphs, and entity graphs.
• Develop and implement strategies for using vector databases and graph databases to enable powerful LLM-augmented search and reasoning.
• Partner with data engineering, AI, and business teams to translate business goals into ontological models.
• Contribute to the development of a unified data access layer that supports querying across diverse data sources.
• Implement and enforce enterprise security models through the ontology and semantic layer.
• Collaborate on AI integration initiatives, including building ontology-driven agents.
• Establish ontology governance processes and contribute to the internal Data Marketplace.

Lead Data and Ontology Engineer

• Design, develop, and maintain scalable data processing pipelines and workflows using frameworks such as Apache Spark, PySpark, and Apache Beam.
• Build and maintain microservices in Python that serve data-driven features in production.
• Develop internal tools to support CI/CD pipelines, experiment tracking, and data versioning.
• Collect, process, and integrate large datasets from multiple sources, including databases, file systems, and APIs.
• Ensure data integrity, consistency, and quality through robust validation and monitoring processes.
• Optimize data systems for performance, scalability, and high availability.
• Implement best practices for data security, access control, and privacy.
• Collaborate with data scientists, analysts, and engineers to support analytics and ML workflows.
• Lead complex migration initiatives involving the transition from on-premise Cloudera environments to cloud-native platforms, ensuring zero data loss and minimal downtime.

• Design, build, and maintain data pipelines and models that power Oura's Corporate Scorecard, revenue reporting, LTV analysis, and RevOps workflows.
• Partner closely with Finance, Sales Operations, and Business Intelligence to understand reporting requirements and translate them into scalable data models.
• Own end-to-end data quality and reliability for financial datasets — from ingestion to consumption-ready tables.
• Integrate and rationalize data across finance source systems (e.g., NetSuite, Salesforce, Pigment, e-commerce platforms) into a unified, governed data layer.
• Ensure financial data pipelines meet compliance, audit, and privacy requirements.
• Reduce manual, spreadsheet-driven reporting processes by automating trusted, self-serve data products.

Senior Data Engineer – Finance

• Design, build, and maintain scalable data pipelines across internal databases, vendor systems, and our data warehouse
• Support both batch and event-driven or CDC-based data workflows
• Improve pipeline reliability, observability, and performance
• Troubleshoot issues in production and proactively solve data quality or system problems
• Build and maintain data warehouse schemas and transformation layers to support analytics and operational use cases
• Communicate clearly about implementation choices, tradeoffs, and technical constraints
• Identify opportunities to improve data workflows through better tooling, automation, and engineering practices
• Help shape scalable patterns for how data is ingested, transformed, stored, and accessed

• Develop and maintain integration pipelines in Azure Data Factory that move and transform data between SaaS platforms and the company's data layer.
• Write, optimize, and maintain T-SQL code — stored procedures, views, functions, and triggers — supporting both integration logic and downstream reporting.
• Design and maintain normalized relational data models in Azure SQL Database and SQL Server.
• Build and maintain integrations with vendor REST APIs, including authentication, pagination, retry logic, and structured error handling.
• Support and extend SQL Server components used for real-time and complex-payload integration scenarios, including C# / CLR stored procedure modules.
• Partner with business analysts to expose curated data through Power BI, leveraging the existing SQL layer (views, functions, semantic models) in a sustainable client/server model.
• Monitor pipelines and integrations for reliability, performance, and data quality; investigate and resolve issues as they arise.
• Document data flows, data models, and integration interfaces.
• Participate in code reviews, technical discussions, and small-team planning.

• Design and develop enterprise data architecture to support the company’s internal operational and analytical data systems.
• Define and maintain database schemas, logical and physical data models, and data structures to ensure data consistency, scalability, and reliability.
• Design and analyze the implementation of data integration and ELT/ETL processes using Python, SQL, and cloud-based platforms to support reporting and analytics needs.
• Review and validate data integration from multiple internal systems and data sources to ensure completeness, reliability, and alignment with centralized data platform standards.
• Evaluate and refine database development standards, ensuring consistent implementation and alignment.
• Monitor database performance and implement optimization strategies to improve efficiency and system reliability.
• Conduct data integrity reviews to uphold security standards and compliance with internal policies and applicable data privacy requirements, especially for our database backup, recovery, and system monitoring procedures.
• Configure, maintain, and support cloud-based database environments, including Citrix Cloud.
• Collaborate with business and technical teams to translate data requirements into effective database and architecture solutions.

• Design, develop, and deploy forecasting models for product demand, pricing trends, and quotation accuracy using GCP-native services
• Conduct exploratory data analysis (EDA) and feature engineering on large-scale datasets
• Build AI agents for forecasting and quotation workflows using agentic frameworks
• Develop and maintain production ML pipelines on Vertex AI Pipelines and Cloud Composer
• Implement statistical experimentation frameworks to validate model improvements
• Collaborate with data engineering teams to design feature stores and data pipelines in BigQuery
• Optimize model performance through hyperparameter tuning and interpretability techniques
• Integrate ML model outputs into agentic workflows
• Document model architectures and present findings to stakeholders

Senior Machine Learning Engineer, Data Science

• Architect and optimize large-scale distributed pipelines
• Scale up inference throughput across the pipeline
• Design systems that reliably store, index, and serve billions of data points
• Apply deep expertise in distributed systems and frameworks
• Partner with modeling teams to understand data that improves training outcomes
• Operate as a hands-on technical leader

Principal Scientist – Data Pipeline Engineer

• Designing and implementing AI systems to transform raw data into actionable insights
• Developing scalable machine learning models to support client decision-making processes
• Collaborating with cross-functional teams to integrate AI solutions into existing platforms
• Conducting data wrangling and preprocessing to prepare datasets for analysis
• Applying advanced algorithms to enhance the performance of AI models
• Building and deploying software systems that leverage AI and machine learning technologies
• Analyzing complex data sets to identify patterns and trends that inform business strategies
• Supporting client engagements by delivering quality work and adapting to diverse project requirements
• Engaging in continuous learning to deepen technical skills and knowledge in AI engineering
• Upholding professional and technical standards while contributing to team success

AI Engineer, AI Experienced Associate

• Size, architect, and design data protection, disaster recovery, and scale-out storage solutions
• Bring new products and processes to life to meet customer demand, improve service SLAs, and minimize gaps
• Serve as the escalation point for L1, L2, and L3 support engineers
• Work directly with customers to solve challenging design and support issues
• Automate the collection of backup system health and streamline tasks
• Monitor backup systems and performance, identifying and troubleshooting issues
• Configure access using a least-privileged approach, audit, and ensure adherence to policies
• Communicate upgrade and rollback plans, and execute upgrades
• Build lasting trust with clients

Data Protection Engineer

• Develops, documents, tests, debugs, and maintains data; provides expertise and familiarity in software tools, frameworks and best practices in secure software engineering.
• Collaborates effectively with other members of the team to deliver on the team’s backlog.
• Participates in the discussion and implementation around complex capabilities.
• Develops complex features and capabilities to enhance the Early Warning core product offerings.
• Manages source code using Git.
• Receives, reviews and approves pull requests ensuring that code quality remains high.
• Processes both real time stream data and file based data through ETL frameworks and persist the data into the data store.
• Builds aggregations on the data to enable provisioning of data to the AI/ML models as input and also deliver them to the customers.
• Document complex details and decisions in Confluence.
• Builds automated tests alongside sprint work that is integrated into the CI/CD pipeline, considering environment variables, and provides recommendations.
• Able to troubleshoot complex bugs that may arise during development.
• Support the company’s commitment to risk management and protecting the integrity and confidentiality of systems and data.

Data Engineer jobs in California

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Staff Data Engineer

Director, Data Engineering – Business Data

Data Architect

Senior Data Engineer

Data Engineer, Payments

Staff Software Development Engineer – MCED, Lab, Data Technology

Software Development Engineer IV – Algorithms, Scientific Data

Data Scientist – Signal Processing Engineer, Acoustics

Lead Data Engineer – Governance

Lead Data and Ontology Engineer

Senior Data Engineer

Senior Data Engineer – Finance

Senior Data Engineer

Data Engineer

Data Architect

Senior Machine Learning Engineer, Data Science

Principal Scientist – Data Pipeline Engineer

AI Engineer, AI Experienced Associate

Data Protection Engineer

Senior Software Engineer – Data Engineering