Top Senior Analytics Jobs in San Diego, CA

Leads enterprise data and analytics strategy, architecture, modernization, and delivery across cloud and on-premises environments. Oversees data platforms, engineering, integration, governance, quality, lineage, security, and compliance. Partners with executives to define roadmaps and investments, manages cross-functional teams and vendors, enables AI/ML capabilities, and mentors data professionals while driving scalable, trusted data solutions and measurable business outcomes.

Manages programmatic analytics and insights for client accounts, leading measurement frameworks, brand lift studies, incrementality testing, and A/B experiments. Develops media and campaign strategies, communicates performance insights to clients, and supports pitches and QBRs. Oversees, mentors, and develops analysts while partnering with Product, Engineering, Sales, and Customer Success teams to improve measurement tools, campaign optimization, and analytical capabilities.

Owns fraud performance analytics for depository products, including monitoring losses, approval rates, false positives, customer friction, and risk metrics. Builds data models, transformation pipelines, dashboards, scorecards, and monitoring frameworks. Conducts fraud deep dives and develops optimized rules, policies, thresholds, models, and decision strategies. Partners with Product, Engineering, Machine Learning, Data Engineering, Fraud Operations, and Risk teams to improve fraud detection and support product launches.

Leads analytics strategy and technical delivery for clients, specializing in Adobe Analytics and Customer Journey Analytics. Conducts measurement workshops, defines KPI and implementation strategies, oversees tagging and data quality, guides experimentation, and reviews dashboards and insights. Advises senior stakeholders, manages and mentors consultants, supports proposals and account growth, and contributes to Adobe Experience Platform initiatives involving Target, RT-CDP, and AJO.

Leads Toast’s analytics engineering function, owning enterprise data architecture, modeling strategy, analytics-layer development, and AI-ready semantic-layer vision. Builds and manages a team of analytics engineers, establishes roadmaps, partners with data engineering and business leaders, and drives scalable, governed data solutions across domains. Requires expertise in data modeling, dbt, SQL, stakeholder alignment, and people leadership.

Serve as Product Owner for Pet Data & Analytics: define vision, roadmap, and backlog; translate business needs into epics/features/user stories; prioritize based on value; collaborate with data engineers, analysts, and stakeholders; ensure data quality, governance, and successful analytics delivery across agile teams.

Owns analytics and optimization strategy for advertising revenue across Zynga’s mobile game portfolio. Responsibilities include monitoring performance, diagnosing trends, designing A/B tests, identifying monetization opportunities, partnering with product, analytics, and data science teams, supporting AI and ML models, leading cross-functional initiatives, and mentoring analysts.

Lead programmatic analytics for clients and internal teams by analyzing campaign and model performance, designing tests, developing measurement solutions, recommending optimizations, and presenting actionable insights. Partner with Sales, Customer Success, Product, Engineering, and clients to align marketing strategies, reporting requirements, and optimization efforts with business goals.

Own end-to-end analytics engineering for AI and developer experience data. Instrument telemetry, build governed production pipelines, define trusted metrics, and create executive-facing dashboards and analyses. Partner with data engineering, AI, and data science teams to improve data quality and connect raw signals to decision-making. Present clear recommendations on AI investment, developer productivity, and ROI while using AI coding agents to build and maintain pipelines, dashboards, and analyses.

Lead and scale Runpod’s analytics and business intelligence function, managing analysts and BI practitioners while setting the analytics roadmap. Own core company metrics, consumption and marketplace analytics, self-service reporting, experimentation practices, and executive and board-level insights. Partner with Product, Finance, Engineering, Security, Supply, and Go-to-Market leaders to improve data quality, modeling, decision-making, and strategic outcomes.

Own end-to-end analytics pipelines for AI usage, developer experience, engineering productivity, and spend. Instrument telemetry, build governed ETL workflows, define trusted metrics, and create executive dashboards and visual narratives. Analyze results, recommend improvements to AI ROI and developer experience, and communicate methodology to senior stakeholders. Collaborate across data engineering, applied AI, and data science while using AI coding agents to build, test, and maintain production data systems.

Design and maintain analytical data models, transformation frameworks, data-quality checks, semantic definitions, and metric consistency across analytics and customer-facing products. Partner with engineering, product, and analytics teams to document data, improve lineage, and enable trustworthy self-service data usage. The role requires advanced SQL, analytics engineering, dimensional modeling, testing, and collaborative translation of business concepts into precise data definitions.

Leads analytics for Pricing and Supply, managing a team of approximately eight analysts. Partners with cross-functional leaders to prioritize analytical work, build dashboards and decision frameworks, improve metric quality, and generate insights into pricing, supplier coverage, marketplace performance, conversion, margin, and customer behavior. Coaches analysts, establishes scalable analytics processes, and collaborates with data engineering teams to improve data models, tooling, and self-service capabilities.

Leads advanced business analytics across Commercial, Finance, and Operations. Builds scalable solutions using SQL, Python, Snowflake, Tableau, and Power BI; performs forecasting, statistical analysis, segmentation, predictive modeling, and exploratory analysis. Develops semantic models, KPI frameworks, data governance practices, and reusable datasets while improving data quality and pipelines. Partners with leadership and technical teams to translate complex data into actionable insights, automate decision support, and influence strategic business outcomes.

Own Hercules’ full data stack, including ETL from third-party providers into BigQuery, data modeling with dbt and medallion architecture, semantic-layer metric definition, and self-service business intelligence in Omni. Build reliable, maintainable systems and dashboards while applying strong software architecture, API design, code quality, and AI-assisted development practices.

Own end-to-end data products, including source onboarding, API and file-feed ingestion, orchestration, incremental and idempotent pipelines, Snowflake and dbt Cloud modeling, testing, documentation, alerting, and production reliability. Establish engineering standards, manage secrets and governance, use AI coding tools responsibly, collaborate with vendors and stakeholders, and mentor junior engineers.

Develop and maintain dashboards, reports, scorecards, KPI solutions, and analytical models that provide business performance insights. Responsibilities include trend and root-cause analysis, forecasting, data validation, stakeholder requirements gathering, reporting automation, process improvement, documentation, and data quality governance. The role partners with cross-functional teams and communicates findings to technical and non-technical audiences.

Own marketing channel performance tracking, reporting, experimentation, dashboards, forecasting, and LTV analysis. Develop measurement frameworks, identify data issues, and translate complex findings into actionable recommendations for Marketing, Growth, Product, and Finance stakeholders. The role requires strong SQL, BI dashboarding, marketing measurement, experimentation knowledge, and effective analytical communication, with AI tools used to accelerate reporting and analysis.

Designs and maintains healthcare analytics infrastructure, production data models, semantic layers, and transformation pipelines using advanced SQL and analytics engineering practices. Integrates payer claims, EMR, clinical, attribution, quality, and operational data. Performs ad hoc analysis, defines standardized healthcare metrics, validates data quality, optimizes queries, documents data flows, and partners with clinical, operational, IT, and payer stakeholders to support value-based care and enterprise reporting.

Designs scalable data models, optimized SQL queries, analytics infrastructure, semantic layers, and data applications. Partners with data science, product, engineering, and business teams to enable dashboards, self-service analytics, reporting, and enterprise data-driven decision-making. The role also establishes analytics engineering best practices, improves developer experience, and supports secure, compliant data warehousing.

Lead growth analytics for Zendesk’s product-led growth motion, guiding a team of analysts and partnering with Product, Engineering, Sales, Marketing, and Data teams. Define funnel metrics, measurement plans, experiments, and instrumentation across acquisition, activation, conversion, retention, and expansion. Analyze customer behavior using segmentation, cohorts, funnels, experimentation, and statistical methods. Improve self-service reporting, dashboards, metric definitions, reusable datasets, and analytics capabilities while coaching analysts and influencing senior stakeholders.

Designs, builds, and maintains enterprise data pipelines, ETL processes, integrations, cloud data infrastructure, and complex data models. Supports analytics partners, develops data warehouse solutions, tunes SQL and database performance, troubleshoots production issues, and delivers end-to-end data engineering projects. The role collaborates with architects, analysts, developers, and business stakeholders, applies big data technologies, supports agile delivery, and mentors team members.

Lead business intelligence and analytics for executive dashboards, planning, performance reporting, and strategic decision support. Own Power BI and Microsoft Fabric semantic models, data products, governance, lineage, and reporting architecture. Conduct root-cause, trend, scenario, and opportunity analyses, translating ambiguous business questions into actionable recommendations. Partner with finance, strategy, sales, operations, engineering, and analytics teams to improve data quality, scalability, self-service analytics, and AI-ready reporting foundations.

Own the customer experience data foundation by designing dbt models, pipelines, data quality tests, documentation, semantic layers, dashboards, and self-service datasets. Integrate support, KYC, withdrawal, CRM, workforce management, and market resolution data into the cloud warehouse. Identify recurring support issues, communicate findings to stakeholders, and partner with data engineering on infrastructure and governance.

Build Polymarket’s CX data and analytics function, defining service and customer experience metrics, creating dbt models and reporting, and delivering actionable insights to leadership. Lead Analytics Engineers, partner with central data engineering on pipelines and infrastructure, and develop measurement frameworks for AI automation. Apply forecasting and modeling to staffing, contact drivers, and AI deflection while communicating recommendations to executive audiences.