Top Senior Analytics Jobs in Sacramento, 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.

Leverage data analytics, business intelligence, and data science techniques to deliver strategic insights and solutions for clients. Build reports, dashboards, data pipelines, visualizations, and data architecture using tools such as Oracle BI, QlikView, Spotfire, and BIRT. Ensure data quality, manage stakeholder relationships, improve analytics processes, interpret complex datasets, and mentor junior team members while contributing to client projects and business strategy.

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.

Leads the design and delivery of governed, business-ready data products, dimensional models, semantic assets, and KPI frameworks for Global Operations. Builds scalable lakehouse transformations using SQL, Python, and PySpark; establishes data quality, lineage, metadata, testing, CI/CD, monitoring, and security practices; and partners with business and technology stakeholders across supply chain, manufacturing, quality, and regulatory functions. Develops AI-ready data foundations supporting analytics, machine learning, generative AI, and executive reporting.

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.

Analyzes healthcare datasets, including claims, pharmacy, utilization, and cost data, to develop reports, dashboards, forecasts, and actionable insights. Builds and maintains SQL-based reporting and data processes, validates data quality, supports regulatory reporting, and identifies automation and process improvements. Partners with stakeholders to define requirements, presents recommendations, supports projects and audits, and provides guidance to other analysts.

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.

Design and implement scalable analytics/data models and dbt pipelines, manage Databricks, build dashboards and BI solutions, partner with business stakeholders, support CRM/marketing analytics, and explore GenAI/ML opportunities to deliver reliable, automated data products.

Build and own commercial data models in Snowflake and dbt for ARR, bookings, pipeline, account scoring, ICP fit, upsell, and whitespace analysis. Establish semantic layers, governance, documentation, testing, monitoring, and CI/CD practices. Partner with Sales, Marketing, Finance, and enterprise data teams to deliver trusted self-service analytics, improve data infrastructure, and develop AI-powered automation. This senior individual contributor will own the roadmap and mentor analysts on data modeling and dbt.

Analyze healthcare, financial, and utilization data to identify inefficiencies, support data-driven decisions, maintain dashboards, build analytical models, communicate insights, and monitor improvement initiatives across the organization.

Own client relationships and analytics strategy across digital measurement and GEO initiatives. Architect, audit, and QA GA4 and Adobe Analytics implementations; develop measurement frameworks, dashboards, experimentation plans, and causal analyses. Present insights to executive stakeholders, manage analytics workstreams, mentor teammates, and build AI-assisted workflows, automations, and scalable systems. The role combines traditional analytics with measurement of AI-powered search visibility and customer behavior.

Lead architecture and execution of Grafana's marketing data stack and analytics practice. Build forecasting, predictive, and causal models, design BigQuery schemas, deploy agentic AI and automated pipelines, partner with GTM leadership, and deliver executive-grade insights to drive demand generation and ROI.

Build and maintain scalable ELT pipelines, dbt models, semantic layers, and governed product analytics assets. Partner with Data Engineering, Product Engineering, Data Governance, analysts, and product managers to define trusted metrics, monitor data quality, document taxonomies, and support self-service analytics. Translate product usage telemetry into certified metrics for expansion and upsell analysis, while communicating technical concepts to technical and non-technical stakeholders and driving engineering best practices.

Build and maintain reliable data models, pipelines, metrics, dashboards, and self-service data products for Airbnb.org. Own data quality, testing, validation, incident response, and improvements to data assets. Partner with Marketing, Fundraising, Programs, Product, Data Science, and Engineering to translate business needs into trusted data solutions. Lead projects from ambiguous scoping through delivery, communicate technical tradeoffs, and mentor fellow data practitioners.

Lead end-to-end analytics for Netflix Ads demand, including media planning and campaign management tools, machine learning models, and agentic solutions. Partner with scientists, product managers, engineers, and data engineering to develop metrics, reliable analytics data, insights, and recommendations. Independently identify risks and opportunities, shape product roadmaps, and communicate findings to technical and non-technical stakeholders.