Top Senior Analysis Reporting Jobs in Sacramento, CA

Analyze and optimize marketing campaign ROI across channels, evaluate app and partner data, conduct funnel, cohort, attribution, and retention analyses, and develop actionable recommendations. The role requires Marketing Mix Modeling, Multi-Touch Attribution, forecasting, segmentation, and statistical analysis. The consultant will collaborate with product, analytics, engineering, and marketing stakeholders to define measurement strategies, build reporting mechanisms, and translate complex data into business insights that improve adoption, retention, revenue, and campaign effectiveness.

Build and maintain dbt and semantic data models in Snowflake, develop ingestion and transformation pipelines, define trusted product metrics, and improve data collection. Partner with Product and Engineering to support experimentation and feature analysis. Establish data quality testing, documentation, HIPAA-aware access controls, and compliance practices. Use and validate AI-generated code and Snowflake Cortex outputs while raising technical standards across the team.

Build customer health scores, churn and expansion analytics, retention dashboards, automated alerts, and executive reports. Maintain integrations among databases, CRM, and BI tools while improving data quality and standardizing reporting. Lead AI adoption for analytics, validate generated insights, and enable customer-facing teams to make informed retention and expansion decisions. Requires strong BI, logistics or supply chain analytics, dashboarding, data integration, communication, and customer-focused skills.

Leads the design, development, and optimization of scalable data pipelines and analytics platforms. Builds ETL/ELT workflows using Databricks and Spark, integrates and transforms source data into curated models, manages SQL Server data, and maintains documentation and lineage. Collaborates with product, analytics, and engineering teams to deliver reliable data solutions. Applies responsible AI practices and delivers projects independently within established timelines.

Develop customer analytics that improve retention, growth, adoption, and operational performance. Build and maintain BI dashboards, customer health metrics, churn-risk reporting, and predictive models. Partner with Client Success, Product, Sales, and Operations to translate complex findings into actionable recommendations. The role also owns data quality, governance, reporting automation, workflow improvements, and stakeholder communication.

Develop and support enterprise BI solutions, including dashboards, reports, governed datasets, data marts, and ETL/ELT processes. Translate business requirements into technical designs, perform data analysis and validation, troubleshoot production issues, and maintain documentation, governance, security, and data-quality standards. Lead-level candidates will guide technical decisions, mentor team members, and coordinate complex initiatives and incidents.

Develop and maintain Power BI reports, dashboards, apps, and reusable semantic models using SQL, DAX, and Power Query. Own day-to-day Power BI platform operations, including workspaces, publishing, licensing, refreshes, gateways, permissions, and content lifecycle. Translate stakeholder requirements into decision-ready reporting experiences, validate data and calculations, support testing and releases, troubleshoot issues, and coordinate with engineering and security teams. Coach report authors and promote governed, reliable self-service analytics.

Analyze and model property catastrophe reinsurance treaties using catastrophe modeling platforms. Prepare and validate exposure data, execute models, interpret risk metrics, develop scenarios, and identify portfolio risk drivers. Support underwriting, actuarial pricing, placement, renewals, and management decisions through clear reporting. Participate in model validation, calibration, research, and development while collaborating with underwriters, actuaries, and other stakeholders.

Conducts econometric, causal, statistical, and optimization analyses to improve operational efficiency, resource allocation, product performance, and business outcomes. The role defines efficiency measures, develops forecasting and optimization tools, partners with product and engineering teams on data instrumentation and implementation, and communicates findings to executives through clear reports.

Build and maintain reusable analytical data products, canonical entities, data marts, semantic layers, and governed metrics across business domains. Establish modeling, documentation, quality, lineage, and change-management standards; improve data reliability, self-service analytics, and production governance. Partner with Data Platform, Trust, Data Science, Applied AI, and business stakeholders on orchestration, observability, access controls, feature tables, model outputs, and shared metric definitions. Remain hands-on with SQL, Python, testing, code reviews, documentation, and technical coaching.