Top Entry Level Data & Analytics Jobs in San Francisco Bay Area, CA

Leads content analytics by designing reporting systems, analyzing performance across digital platforms, generating data-driven insights, advising content teams, automating data collection and analysis, and presenting findings to senior and non-analytical audiences. The role also uses experimentation, data science techniques, LLM prompting, and agentic AI tools to improve content performance.

Own Modal’s go-to-market data platform, including organization and people graphs, identity resolution, CRM schemas, Snowflake and dbt models, reverse ETL pipelines, revenue operations metrics, forecasting, attribution, and data quality systems. Partner with Data, RevOps, and Marketing to support customer context, routing, territory management, campaigns, reporting, and reliable GTM workflows.

Conduct media, social, and policy research to identify reputational issues, risks, and narratives. Prepare alerts, briefings, reports, presentations, and recommendations for clients. Support monitoring, benchmarking, communications measurement, data organization, quality control, and analysis using Excel and research tools. Collaborate with account teams, participate in client meetings, manage deadlines, incorporate feedback, and help improve research quality.

Build and own Shepherd’s canonical analytical layer using dimensional modeling and dbt. Partner with actuarial, reporting, operations, engineering, and other data consumers to define trusted business entities, facts, dimensions, relationships, and rules. Establish testing, reconciliation, documentation, lineage, and adoption practices; evaluate and migrate existing models and datasets; and shape semantic context for human and AI-assisted analytics.

Build and scale foundational data infrastructure for AI-powered hardware and consumer devices. Responsibilities include developing reliable pipelines, integrating manufacturing, telemetry, quality, and customer operations data, creating durable data models, enabling manufacturing and product intelligence, and establishing data engineering standards. The role requires strong architectural judgment, distributed systems knowledge, production infrastructure experience, and cross-functional collaboration in an early-stage organization.

Conducts exploratory and defined analyses of criminal justice datasets, identifies trends and discrepancies, prepares analytical data for state product launches, and builds documented visualizations and dashboards. The analyst communicates findings to internal teams and state partners while collaborating with senior analysts and engagement teams. Responsibilities include cleaning real-world data, supporting product launches, evaluating product impact, and developing expertise in assigned states’ data, policies, and products.

Own and refine financial and profitability metrics, build curated data models, dashboards, and analytical views, and analyze large-scale financial data. Partner with Finance, Product, Operations, and Data teams to support planning, capital allocation, KPI development, reconciliation, and data quality. Design GenAI-powered self-service analytics tools and communicate insights, assumptions, and recommendations to technical and senior business stakeholders.

Owns dataset strategy and quality for physical labor AI data. Defines the ideal dataset, objective functions, taxonomy, golden sets, acceptance criteria, audits, and incremental data value. Converts these frameworks into collection strategies, operational standards, and vendor requirements while optimizing information gain over unnecessary fidelity or volume. Partners with marketplace, software, research, and evaluation teams to connect dataset quality with model capability.

Build and operate data pipelines that integrate customer financial data from ERPs, warehouses, and payment systems. Develop AI agents that automate ERP implementations, partner with implementation teams to understand and transform customer data, and contribute to shared AI platform capabilities. The role requires strong full-stack engineering fundamentals, autonomous execution, and comfort working in an ambiguous, fast-moving environment.

Own Mercor’s data platform infrastructure across Snowflake, Fivetran, Airflow, Astronomer, and Hex. Responsibilities include data organization, role-based access control, row-level security, PII masking, governed migrations, pipeline reliability, on-call support, dashboard governance, warehouse cost optimization, and CDC or streaming ingestion. The role also establishes platform standards and builds internal tooling for a new Data Platform team.