Analytics Engineer

• Build and maintain interactive dashboards and standardized reports in the enterprise BI platform (e.g., Power BI, Tableau) that address recurring commercial reporting needs.
• Translate business user requirements into clear, accurate, and usable visualizations aligned to approved data models and metric definitions.
• Apply consistent design standards, layout conventions, and UX best practices to ensure reports are intuitive and accessible to non-technical users.
• Perform unit testing and validation of report outputs against source data to ensure accuracy before deployment.
• Build and maintain semantic layer objects (e.g., measures, dimensions, hierarchies, calculated fields) within the governed BI and analytics platform.
• Ensure semantic objects conform to approved business taxonomy, metric definitions, and naming conventions.
• Gather and clarify requirements from commercial business users to ensure dashboard and reporting solutions meet their analytical needs.
• Provide first-line support for BI platform questions, report issues, and data interpretation queries from business users.
• Monitor dashboard and report data freshness, flagging anomalies or stale data to the appropriate pipeline owners.
• Support the development and testing of agentic AI workflows that leverage governed semantic layer assets and BI data products for automated commercial insights delivery.

Commercial Analytics Engineer – Business Decision Intelligence

• Experiment with and combine data from a variety of data sources and with increasing complexity for use in analytics & reporting solutions.
• Own and maintain the MyInsights reporting portal, ensuring analytics and reporting solutions are organized, accessible, and aligned with customer needs and business priorities.
• Supports user training, acts as an ambassador of the solution, and partners with project teams to ensure smooth and launch and adoption
• Elicit and clarify business needs in order to design and develop appropriate analytics & reporting solutions that satisfy those needs
• Update and maintain analytics & reporting solutions according to industry standard methodologies
• Find opportunities to consolidate analytics & reporting solutions
• Establish and monitor adoption criteria based on utilization statistics
• Maintain relationships with customer groups and respond to business needs as applicable under the guidance of more senior associates
• Technical design, build and iteration of analytics & reporting solutions.
• Intakes feedback on prototypes and iterates continuously to improve user experience and customer engagement.
• Partners with IT and other tech vendors and partners for necessary technical development and support as required.
• Proposes mock and prototype low-fidelity designs using design thinking methodology.
• Understands how the solutions in the team work together and finds opportunities to drive consistent service experience across the suite of solutions.
• Work with customer teams to ensure proper insights are extracted from solutions
• Work in close partnership with more senior Associates, who will provide guidance and technical direction to build the Analyst’s skills.
• Develop more complex solutions, seeking the expertise of more senior associates as needed.

Senior Engineer, Analytics & Productivity Solutions

• Experiment with and combine data from a variety of data sources and with increasing complexity for use in analytics & reporting solutions.
• Own and maintain the MyInsights reporting portal, ensuring analytics and reporting solutions are organized, accessible, and aligned with customer needs and business priorities.
• Supports user training, acts as an ambassador of the solution, and partners with project teams to ensure smooth and launch and adoption.
• Elicit and clarify business needs in order to design and develop appropriate analytics & reporting solutions that satisfy those needs.
• Update and maintain analytics & reporting solutions according to industry standard methodologies.
• Find opportunities to consolidate analytics & reporting solutions.
• Establish and monitor adoption criteria based on utilization statistics.
• Maintain relationships with customer groups and respond to business needs as applicable under the guidance of more senior associates.
• Technical design, build and iteration of analytics & reporting solutions.
• Intakes feedback on prototypes and iterates continuously to improve user experience and customer engagement.
• Partners with IT and other tech vendors and partners for necessary technical development and support as required.
• Proposes mock and prototype low-fidelity designs using design thinking methodology.
• Understands how the solutions in the team work together and finds opportunities to drive consistent service experience across the suite of solutions.
• Work with customer teams to ensure proper insights are extracted from solutions.
• Work in close partnership with more senior Associates, who will provide guidance and technical direction to build the Analyst’s skills.
• Develop more complex solutions, seeking the expertise of more senior associates as needed.

• Design, develop, and maintain data pipelines, integrations, and automated data workflows
• Build and support data lake, data warehouse, Databricks, and analytics platform solutions
• Develop, optimize, and maintain ETL/ELT processes to ingest, transform, and organize data from internal and external systems
• Create and maintain analytics-ready datasets, semantic models, and data structures optimized for Power BI consumption, in collaboration with BI Analysts on semantic model design
• Develop and maintain data transformation workflows using SQL, Python, Databricks, and other modern data platform tools
• Own operational health of the data platform, including monitoring, alerting, incident response, and continuous reliability improvements
• Implement data validation, testing, reconciliation, and monitoring processes to ensure data accuracy and integrity
• Develop and maintain technical documentation, data dictionaries, lineage documentation, and operational procedures
• Collaborate with application developers, BI analysts, and business stakeholders to define data requirements and deliver data solutions
• Establish and promote data engineering standards, best practices, and governance processes
• Evaluate and recommend improvements to data architecture, tools, processes, and technologies
• Administer database and data platform security, including access management, role-based permissions, and auditing of data access
• Ensure data solutions comply with privacy, regulatory, and data governance requirements
• Manage database and platform operations including backup, recovery, disaster recovery planning, and environment management
• Monitor and optimize platform compute utilization and cost, including Databricks cluster configuration and workload efficiency
• Implement data retention, archival, and cataloging policies
• Participate in solution design, architecture reviews, and strategic data initiatives

Data Platform & Analytics Engineer

• own the long-term architecture and evolution of Cribl's analytics engineering platform
• design, build, and maintain certified dbt models as the authoritative source for business-critical metrics
• establish and enforce analytics engineering standards for modeling, testing, documentation, and code review
• design and maintain semantic and metadata layers that enable reliable AI-powered analytics and self-service
• partner with analysts to migrate high-value business logic from Omni into governed warehouse models
• partner with Data Engineering to improve source reliability, warehouse architecture, and Snowflake performance and cost efficiency
• mentor analysts and analytics engineers on dbt development, data modeling, and analytics engineering best practices
• work may happen across many time zones

Staff Analytics Engineer

• Develop perception algorithms for re-id of vehicles on massive construction sites
• Extend, modify and adapt existing perception signals for analytics needs
• Work with customers to define and scope what is possible with advanced perception systems on-site
• Deploy models and analytics directly to fleets of machines

Machine Learning Engineer – Perception Analytics

• Build canonical data models that create a shared source of truth across the company
• Define and maintain core business, operational, financial, product, and customer-facing metrics
• Model data in dbt or equivalent transformation tooling so dashboards, self-serve analytics, and customer reports pull from trusted tables
• Write tests, documentation, and data quality checks that catch issues before they reach users
• Create clear definitions for tables, fields, and metrics so teams understand what the data means and when to use it
• Reconcile metric definitions across internal teams, external reporting needs, and payer customer expectations
• Trace data lineage and debug dashboards, reports, or tables that change unexpectedly
• Partner with analysts, data scientists, operations, finance, product, engineering, and customer-facing teams to understand data needs and translate them into reliable models
• Help build reusable reporting frameworks that make onboarding new payers faster and less manual
• Partner with the data platform team to evolve warehouse tables, improve data architecture, and strengthen data contracts
• Improve warehouse cost, performance, and maintainability
• Support PHI-aware data access patterns and help ensure sensitive healthcare data is modeled and used responsibly

Senior Analytics Engineer

• Own a product surface end to end — from customer conversations and product design through API design, implementation, and rollout
• Design and ship alerting and analytics that are granular, flexible, and fast enough to carry contractual latency SLAs
• Build the entitlement and spend-control primitives (credit balances, allocations, usage-percentage alerts, rate limits) that enforce against Orb's real billing state in real time
• Shape the roadmap, primitives, and interfaces other teams build against — foundational decisions that are still open
• Collaborate across PM, design, sales, and customer success, and work directly with enterprise and AI customers to turn their pain points into what we build

Staff Software Engineer, Real Time Analytics

Software Engineer, Real Time Analytics

Senior Software Engineer, Real Time Analytics

• Work at the intersection of data engineering and analytics, partnering with analysts and stakeholders across the business to turn raw data into trusted, reusable datasets.
• Model data from a broad ecosystem of sources on our central data platform.
• Help define and validate key metrics, ensuring the data our models, dashboards, and AI tools depend on is accurate, reliable, and well documented.
• Contribute to semantic layer and metric governance, ensuring definitions are consistent, documented, and reliable across reporting surfaces.
• Mentor junior and intermediate analytics engineers through code review, pairing, and knowledge sharing.

• Design, develop, and support artificial intelligence, intelligent automation, data engineering, analytics, and operational intelligence solutions across Clean Energy Fuels.
• work across the full solution lifecycle—from business requirements and architecture through development, deployment, and support

Senior Data Science, AI & Analytics Engineer

• Collaborate with domain teams to implement a hub & spoke data architecture
• Develop the core Analytics layer in our data warehouse that serves as the hub
• Build an AI-ready semantic layer that is extendable across business domains
• Partner with business stakeholders and our product team to understand their data analytics needs
• Work closely with the data engineering team to ensure scalability across the platform
• Provide technical mentorship and leadership to team members

• Influence technical strategy: Define and drive the long-term technical roadmap for Affirm’s Lakehouse Platform across Apache Iceberg, Spark, Snowflake, and cloud-native storage, balancing scalability, reliability, governance, performance, and cost.
• Design and develop: Architect and implement platform capabilities that make analytical data secure, trustworthy, discoverable, and easy to use across Affirm’s engineering, analytics, machine learning, and business teams.
• Strengthen governance and access controls: Design and operate secure, auditable data access capabilities across Snowflake and the lakehouse platform, including RBAC, dynamic data masking, cataloging, lineage, classification, and privacy policy enforcement.
• Improve analytics engineering foundations: Partner with Analytics Engineering to evolve data modeling, transformation pipelines, testing frameworks, documentation standards, and data quality practices that enable trustworthy self-service analytics.
• Operate at scale: Establish best practices for lakehouse operations, including schema evolution, table maintenance, partitioning, compaction, observability, incident response, production support, and readiness for on-call operations.
• Optimize performance and cost: Identify and execute improvements across analytical compute and storage, including Snowflake warehouse tuning, query optimization, storage layout, lifecycle management, cost attribution, and operational efficiency.
• Collaborate cross-functionally: Partner with Infrastructure, Lakehouse Analytics, Analytics Engineering, Machine Learning, BI, Product Engineering, and SRE to translate stakeholder needs into durable platform architecture.
• Innovate: Stay ahead of industry trends in lakehouse architecture, open table formats, analytical compute engines, data governance, privacy engineering, semantic layers, agentic data tools, and AI-ready data infrastructure.
• Build teams: Mentor engineers, raise technical quality, and foster an inclusive culture of design rigor, operational excellence, and continuous learning.

Staff Software Engineer, Backend – Lake Analytics Platform

• Lead the design and implementation of our semantic layer and data modeling strategy.
• Ensure that data from SAP (S/4HANA, Ariba, BRIM, ME), Workday, Salesforce, and Anaplan is transformed into clean, performant, and "AI-ready" datasets.
• Design and maintain a unified semantic model that provides a "single source of truth" for cross-functional stakeholders, AI Agents, Self Serve Analytics and Executive dashboards.
• Collaborate with Data & AI Engineers to structure and optimize the data that allows to query enterprise knowledge with high accuracy and low latency.
• Establish organizational standards for data modeling, version control, testing, and documentation to ensure high data quality and system maintainability.
• Implement automated testing and observability frameworks that proactively identify data anomalies and "self-heal" pipelines.
• Partner with cross-functional business leaders to translate complex operational requirements into high-impact, scalable data solutions.

Lead Analytics Engineer – Enterprise, Data & AI

• Consult with teams across the organization to translate key product and business processes into technical data requirements. Use dbt, BigQuery, and SQL to build up Packback’s metrics layer to enable self-serve access to core analytics.
• Build, optimize, and orchestrate data pipelines with dbt, Airflow, and Python
• Establish data integrity standards and SLAs to ensure timely, accurate delivery of data
• Build insightful and reliable dashboards to track performance of core company metrics across the product and business
• Collaborate with business operations teams to define and train on best practices with respect to data modeling, analytics, and visualization
• Collaborate with leadership to conduct analyses and apply statistical methods to answer day-to-day questions or support larger initiatives
• Work within Packback’s research function with partner institutions and perform in-depth analyses to understand how Packback’s products are driving key student outcomes
• Consult on the adoption of new technologies that impact Packback’s data infrastructure

• Build and operate core platform capabilities: Design and implement platform features that enable engineers across Affirm to build, deploy, and operate data applications at scale — covering automated provisioning, deploy pipelines, access control, service lifecycle management, and reliability tooling.
• Develop integrations: Build and maintain integrations between the platform and internal systems — CI/CD pipelines, identity and secrets management, external APIs, and event-driven hooks — so that application teams have secure, reliable, and self-service access to the tools they need.
• Strengthen data access and governance: Design and operate secure data access patterns across the platform's analytical infrastructure, including RBAC, managed-access schemas, dynamic data masking, secure views, and cross-database grants that make platform data trustworthy without creating operational bottlenecks.
• Automate toil and unlock self-service: Identify recurring support patterns — access provisioning, data pipeline failures, service health management, secrets management — and build the automation and tooling to make them self-service for platform users, eliminating manual admin work.
• Improve observability and incident response: Build telemetry, alerting, and log infrastructure so platform teams and application owners can diagnose and resolve issues independently. Establish runbooks and operational patterns that reduce blast radius and recovery time.
• Support platform operations: Participate in on-call coverage, triage production incidents across platform services and the applications that run on them, and execute platform-level maintenance with care for the engineers and workflows that depend on the platform.
• Contribute to the emerging platform roadmap: Help design and build next-generation platform capabilities — semantic layer infrastructure, agentic application patterns, and AI-native data tooling — as the platform grows to support Affirm's most ambitious data use cases.
• Raise the engineering bar: Set and hold high standards in code review, design, operational readiness, and incident follow-through. Help establish patterns that other contributors can follow and build on.

Senior Software Engineer, Backend – Lake Analytics Platform

• Turn product and business data into clear, trustworthy insights leaders can act on.
• Own the analytics lifecycle—from ingestion and modeling to BI visualization and decision enablement.
• Deliver self-serve insights using SQL/Python and embedded BI (e.g., ThoughtSpot).
• Define the design patterns and data infrastructure to scale.
• Partner closely with the CX team to quantify and communicate Laurel’s ROI, and join customer-facing presentations.
• Apply machine learning to real-world product and business problems.
• Comfortable prototyping AI/ML models in notebooks, experimenting with approaches (classification, clustering, regression, NLP, etc.), and translating findings into actionable insights.

Senior Analytics Engineer / Data Scientist

• Build dashboards, reports, and lightweight internal applications to support business decision-making
• Work with teams across Engineering, Operations, Finance, People, GTM, and Program Management to understand workflow and reporting needs
• Translate business questions into data models, queries, dashboards, and actionable insights
• Develop tools that improve visibility into company operations, program execution, recruiting, finance, and team performance
• Help standardize reporting workflows and reduce manual spreadsheet-based processes
• Build and maintain lightweight software tools, scripts, integrations, and automations
• Use modern AI-assisted development tools to rapidly prototype and iterate on internal solutions
• Connect systems through APIs, databases, spreadsheets, and workflow automation tools
• Develop simple front-end interfaces, data visualizations, and back-end services as needed
• Improve existing workflows by automating repetitive tasks and simplifying access to key information
• Pull, clean, organize, and transform data from internal systems and business tools
• Support data quality, consistency, and documentation across business-critical workflows
• Build integrations between tools such as HRIS, ATS, finance systems, CRM, project management software, and internal databases
• Partner with stakeholders to validate data accuracy and ensure tools are reliable and easy to use
• Help create repeatable processes for analytics, reporting, and operational tracking
• Work closely with business and technical teams to understand needs, gather feedback, and improve solutions
• Communicate clearly with non-technical stakeholders and translate requirements into practical technical outcomes
• Move quickly from concept to prototype while maintaining good engineering practices
• Document tools, workflows, assumptions, and handoff instructions
• Support a culture of practical, high-ownership problem solving

Software Engineer – Business Analytics

• Apply advanced statistical theory to quantify, analyze, and manage data, financial risks, and market dynamics through robust modeling approaches
• Collaborate and partner with internal and external stakeholders to gather requirements and deliver impactful, data driven and AI enabled solutions
• Engage with Audit, Model, Product, and technical stakeholders to ensure transparency, governance, and compliance with regulatory expectations
• Establish and promote best practices across the model lifecycle, including development, evaluation, validation, deployment, and monitoring, while contributing to analytical strategies, modeling approaches, and forecasting methodologies through execution of complex model development and implementation
• Drive innovation through proof-of-concept initiatives, experimentation, and applied research, while staying current on emerging trends in Generative AI, large language models (LLMs), and Natural Language Processing (NLP), and translating insights into practical business applications
• Guide and mentor junior team members, fostering technical excellence, collaboration, and knowledge sharing
• Lead the design, architecture, and implementation of scalable artificial intelligence (AI), machine learning (ML), Natural Language Processing (NLP), and large language model (LLM) solutions using advanced techniques, modern frameworks, and cloud native tools to support complex language understanding use cases

Analytics Engineer jobs in California

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Commercial Analytics Engineer – Business Decision Intelligence

Senior Engineer, Analytics & Productivity Solutions

Senior Engineer, Analytics & Productivity Solutions

Data Platform & Analytics Engineer

Staff Analytics Engineer

Machine Learning Engineer – Perception Analytics

Senior Analytics Engineer

Staff Software Engineer, Real Time Analytics

Software Engineer, Real Time Analytics

Senior Software Engineer, Real Time Analytics

Senior Analytics Engineer

Senior Data Science, AI & Analytics Engineer

Staff Analytics Engineer

Staff Software Engineer, Backend – Lake Analytics Platform

Lead Analytics Engineer – Enterprise, Data & AI

Analytics Engineer

Senior Software Engineer, Backend – Lake Analytics Platform

Senior Analytics Engineer / Data Scientist

Software Engineer – Business Analytics

Senior Quantitative Analytics Specialist – Senior GenAI Data Scientist