Top Data & Analytics Jobs in Sacramento, CA

Analyze automotive marketing and inventory data using complex SQL queries, investigate CRM and data discrepancies, standardize external data sets, maintain client-facing BI dashboards, and perform rigorous data quality assurance. The role also involves resolving Jira and HubSpot tickets, fulfilling ad hoc analytical requests, and collaborating with a US-based reporting team in English.

Build and maintain ETL/ELT pipelines, ingest and transform structured and unstructured data, integrate APIs and external sources, improve data quality and monitoring, support dashboards and ML pipelines, optimize warehouse and cloud performance, and contribute to data governance and documentation.

Support analytics by cleaning, validating, and transforming data; build dashboards and KPI reports; perform trend, cohort, and A/B testing analyses; improve data pipelines and forecasting; present insights to cross-functional teams.

Design and implement end-to-end cloud data solutions, lead or mentor engineering teams, ensure performance, security, and scalability, integrate diverse data sources, write and optimize SQL, program in Java/Python/Scala, produce presentations and detailed solution documentation, and engage directly with clients to deliver production-ready systems.

Analyze and reverse-engineer legacy tax data for conversion into TaxSys. Create data mapping specifications for developers, QA conversion results, investigate data issues, prepare data for customer training and verification, and guide clients through conversion decisions and go-live. The role also involves customer communication, project documentation, planning, time management, and potential project leadership.

Early-career Data Engineering Analyst supporting AIG’s Data Office through a rotational program. Responsibilities include developing cloud-based data pipelines, collecting and transforming data, building modern software systems, and supporting analytics and data science use cases. The role emphasizes data quality, security, compliance, collaboration, analytical problem-solving, and professional development through mentoring, masterclasses, and on-the-job experiences.

Build and support enterprise data lakes, lakehouses, ETL/ELT pipelines, streaming platforms, and business intelligence solutions. Responsibilities include processing large-scale data, implementing data quality, governance, metadata, lineage, monitoring, anomaly detection, and observability frameworks, and supporting production Linux-based distributed data platforms. The role also provides technical leadership, mentorship, code and design reviews, performance optimization, and support for secure handling of sensitive information.

Own data ingestion, transformation, exchange, and integration workflows between K–12 Student Information Systems and Timely’s scheduling platform. Ensure pipeline accuracy, security, reliability, and scalability while collaborating with engineering, product, customer success, district IT teams, and SIS vendors. Develop documentation, process improvements, automation scripts, and lightweight Python tools; lead discovery for new SIS integrations and help scale the data integrations function.

Analyze business performance, develop hypotheses, answer critical business questions, and build dashboards and reports in Sigma. Support budgeting, forecasting, data quality checks, and reconciliations while partnering with teams across growth, product, insurance, finance, and leadership. Serve as a trusted data advisor by answering metric and reporting questions and promoting data-driven decision-making.

Own go-to-market funnel reporting, dashboards, data quality, and analysis for Sales, Marketing, and RevOps. Integrate CRM and marketing data, monitor lead scoring and attribution models, investigate funnel performance, identify root causes, and provide actionable recommendations. Maintain reporting systems, audit data hygiene and tool adoption, and communicate insights clearly to non-technical stakeholders in a remote-first SaaS environment.

Own experimentation analytics across Product and Marketing, including A/B and multivariate test design, causal inference, statistical analysis, and business recommendations. Build dbt models, dashboards, metrics, and experimentation frameworks; improve data infrastructure and self-service reporting; partner with engineering on instrumentation; mentor analysts; and maintain the analytics roadmap while supporting cross-functional stakeholders.