Top Senior Analytics Jobs in Sacramento, CA

Owns the end-to-end architecture, development, infrastructure, data pipelines, and Power BI reporting platform supporting data governance, informatics, analytics, auditing, and data quality. Leads and mentors a data engineering team, defines schemas and engineering standards, ensures security and HIPAA compliance, oversees testing and Agile delivery, manages stakeholder requirements, and remains hands-on with backend technologies, data models, Power BI, and DAX.

Lead design of end-to-end analytics and data solutions, produce architecture diagrams and technical designs, guide delivery teams, run client discovery and requirements sessions, support pre-sales and proposals, build reusable frameworks, and manage and mentor a delivery team to ensure quality and alignment to architecture.

Design and implement ad measurement metrics, perform deep-dive analyses, build reporting and automated pipelines, create visualizations, and provide actionable insights to improve advertiser measurement and platform health.

Lead analytics for Wealth Solutions: analyze client segmentation, revenue, and operational data; build models for capacity and workforce planning; produce insights and recommendations for senior leadership; partner with BI and Finance to improve advisor productivity, client experience, retention, and revenue growth; support strategic projects and translate complex data into clear presentations.

The Senior Manager of Risk Analytics will lead credit risk strategies, manage portfolio performance, optimize decisioning frameworks, and drive business growth through analytics and predictive modeling.

Translate web and marketing behavior data into actionable insights: measure website and marketing performance, analyze funnels and journeys, design and evaluate A/B tests, build reports and dashboards, monitor analytics integrity, and partner with cross-functional teams to define measurement and optimization opportunities.

Lead a marketing analytics team to deliver acquisition, engagement, retention, and reactivation measurement. Own SQL-based analyses, experiments, audience strategies, dashboards, and incrementality methods. Translate business questions into analytical plans, partner cross-functionally, mentor analysts, and connect marketing activity to downstream outcomes like retention, revenue, and lifetime value.

Lead a team delivering product and business analytics for Fetch's commerce and shoppable experiences. Own prioritization, experimentation, measurement, KPI dashboards, and stakeholder-facing insights to inform product, marketing, and commercial decisions. Manage and develop analysts while partnering cross-functionally to ensure data quality, repeatable analyses, and actionable recommendations.

Hands-on technical lead designing and delivering scalable, secure Azure-based BI and analytics solutions. Own end-to-end Power BI architecture, mentor engineers, guide implementations, optimize performance, and advise clients on governance, deployment, and AI-augmented analytics. Drive discovery, backlog priorities, cross-team alignment, reusable assets, and consulting pursuits.

Bridge enterprise customers' legacy BI to Hightouch by reverse-engineering complex marketing reports, building reusable reporting frameworks, engineering and documenting semantic/context layers, ensuring accurate SQL generation, and training non-technical marketers while collaborating cross-functionally to deliver reliable, idempotent AI-driven analytics.

Own and evolve the analytics engineering platform: design and maintain certified dbt models, enforce modeling/testing/documentation standards, build semantic and metadata layers, optimize Snowflake performance, partner with analysts and data engineering to migrate business logic and ensure reliable, AI-ready analytics, and mentor teammates on analytics engineering best practices.

Lead end-to-end funnel analytics and optimization for SMB lending, identifying friction and bottlenecks, designing measurement frameworks, running experiments (A/B testing), and partnering with Risk, Operations, Product, Engineering, and AI teams to deploy automation and AI solutions that improve conversion, cycle time, and customer outcomes while managing risk.

Hands-on engineering role to design, build, and maintain scalable ETL/ELT data pipelines, data warehouses and lakes, and data models. Optimize batch and near-real-time processing, implement query optimization and data quality checks, support AI data workflows, and collaborate with architects, analysts, and stakeholders.

Lead development of scalable marketing data models, pipelines, and BI solutions. Partner with Marketing to define metrics, build dashboards, enable self-service analytics, and drive data governance and best practices.

Lead Adobe Analytics architecture and MarTech ecosystem design, implement data layers and tagging across web and mobile, ensure QA and privacy compliance, enable reporting and audience activation, and drive cross-functional measurement and governance for personalization and optimization.

Design and implement a scalable Elasticsearch-based analytics platform integrated with an Oracle data warehouse. Architect data models, ingestion/CDC pipelines, index/shard strategies, and monitoring. Optimize cluster, queries, and caching for sub-second dashboard performance, perform load testing and capacity planning, and produce operational documentation and SLAs.

Own and build customer health, usage/adoption, and product configuration data models. Produce ad-hoc and recurring analyses to identify upsell/expansion opportunities, partner with CS, Sales, Product, and Data teams, and present actionable insights to leadership to drive revenue-relevant actions.

Lead a team of analysts to advance fraud and safety operations through analytics, ensuring robust data products and compliance while empowering stakeholders with actionable insights.

Design and maintain SQL-based data pipelines and Power BI semantic models to transform ERP and manufacturing data into actionable dashboards and automated analytics. Partner with stakeholders to improve reporting accuracy, resolve data quality issues, support ERP modernization, and apply automation and AI tools to enhance operational decision-making.

Design, build, and operate the credit fund's end-to-end data and automation stack. Implement deal workflow, ingest borrower financials and payment telemetry, develop Python credit scoring and forecasting models, ship audit-grade investor and borrower dashboards, enforce security and auditability, and move into portfolio monitoring and underwriting while presenting results to LPs and borrowers.

Lead architecture and delivery of enterprise data transformation on the Guidewire Data Platform. Drive design, ingestion/egress, migration performance, and AI-enabled tooling. Facilitate inception and design review sessions, mentor teams, coordinate with partners, and ensure successful data platform adoption and repeatable delivery practices while spending significant time on customer sites.

Design, build, and maintain scalable data systems and ETLs on GCP; connect production data to business systems; generate analytics and ML-driven insights for GTM teams; work cross-functionally and introduce modern tools (Airflow, DBT, Presto, Hightouch).

Lead and own the population health analytics pipeline: intake and reconciliation of claims, eligibility, and MAO-004 data; identify and reconcile care gaps and closures; deliver practice-level performance analysis and billing reconciliations; build funnel instrumentation; document, standardize, and automate pipelines with Product and Engineering. Ensure billing-grade accuracy and support program measurement aligned to CMS reconciliation windows.

Design and implement scalable data platforms, build ETL/ELT pipelines, integrate multiple data sources, define KPIs, create dashboards and executive reports, ensure data quality/governance, and collaborate with stakeholders to optimize BI capabilities.

Lead analytics partner to Community Support, driving data-driven roadmaps, scalable reporting and measurement, causal inference and experimentation, and improvements to agent/guest/host experiences via analytics, modeling, and stakeholder influence.