Top Analytics Jobs in San Diego, CA

Own Silver and Gold medallion-layer modeling and semantic layer governance using dbt and SQL. Improve warehouse performance and cost, standardize BI tooling, collaborate with stakeholders to produce trusted metrics, troubleshoot data quality, and evolve analytics platform.

Embed with agency teams to turn underused data (call transcripts, CRM activity, financials) into structured, actionable insights. Build repeatable pipelines and analytics products, push trusted metrics into the semantic layer, partner with Analytics Engineering and Data Science, and track insight-to-action outcomes tied to client accounts.

Design and maintain scalable dbt data models and ELT pipelines in Snowflake. Architect data foundations to power AI and LLM-enabled workflows, operationalize core SaaS metrics, and build reusable data assets for analytics and activation. Partner cross-functionally to embed analytics into workflows and ensure scalable, secure architecture.

Design and maintain scalable dbt data models and ELT pipelines in Snowflake; architect reusable data assets, governance, and SaaS metrics; build AI-enabled data foundations and LLM workflows; integrate analytics into operational tools and partner cross-functionally to productionize AI-driven use cases.

Own and operate the analytics platform: design and deploy the semantic/metrics layer, administer the data catalog and self-service BI tooling, optimize Amazon Redshift performance, ensure platform reliability/observability, manage security and access policies, and lead tool migrations and vendor evaluations to enable self-service analytics at scale.

Leads the design and delivery of scalable analytics solutions using Azure Databricks, Power BI, and related platforms. Transforms and validates structured and unstructured data, applies statistical techniques, develops dashboards and analytical models, and implements data governance and quality processes. Partners with business and technology stakeholders to translate requirements into technical solutions, provides peer guidance, monitors data trends, and communicates actionable insights to technical and non-technical audiences.

Leads data strategy and analytics for the Risk/Credit team, translating complex datasets into insights for Credit, Operations, Marketing, Product, and Finance. Responsibilities include automating Python/R workflows, building Tableau dashboards, partnering with Data Engineering on Databricks workflows, conducting strategic analyses, monitoring data quality, presenting recommendations to leadership, and documenting analytical methodologies. The role requires strong SQL, cloud data warehouse, visualization, analytics programming, distributed computing, and version-control expertise.

Lead the firm’s analytics strategy, roadmap, and growing analyst team. Partner with executives and law firm operations leaders to define KPIs, evaluate performance, identify bottlenecks, build forecasting and predictive capabilities, and translate data into business actions. Guide SQL, Python/R, statistical modeling, visualization, data quality, and analytics infrastructure standards while partnering with technology and data engineering teams to embed insights into operational workflows.

Design and develop scalable analytics tools, dashboards, evaluation frameworks, and observability systems for promotional media and marketing workflows. Partner with machine learning engineers, data scientists, product managers, and business stakeholders to monitor algorithm adoption, pipeline health, performance drift, and SLA adherence. Translate ambiguous creative and quality requirements into measurable objectives, improve recommendation and asset-generation models, and use data insights to guide business decisions.

Build and optimize end-to-end data pipelines, production-grade dbt models, BigQuery schemas, Fivetran integrations, and Looker semantic layers. Partner with clinical, operations, finance, and growth teams to translate business questions into analytics projects focused on acquisition, attribution, conversion, and funnel performance. Establish data quality, governance, documentation, testing, and version-control practices while mentoring analysts and supporting secure, compliant data use.

Own end-to-end analytics delivery for a client POD, translating business priorities into workplans, coordinating cross-functional teams, governing delivery and revenue KPIs, and managing risks through resolution. Perform hands-on data profiling, validation, reconciliation, exploratory analysis, automation, and quality reviews using SQL, Python, Databricks, and Excel. Create executive-ready KPI, revenue, and business-impact narratives, prioritize initiatives, support business cases, and identify opportunities for analytics automation and AI-enabled improvements.

Own the digital measurement ecosystem across websites, quote engines, customer portals, and account applications. Lead Google Tag Manager tracking frameworks, data-layer standards, Google Analytics 4 configuration, conversion events, attribution, funnel analysis, experimentation measurement, data-quality audits, and analytics governance. Partner with Marketing, Product, Engineering, and Data teams to translate customer behavior into actionable insights that improve growth, conversion, and customer experience.

Leads Zendesk’s Marketing Analytics function, setting the measurement strategy across reporting, attribution, forecasting, experimentation, data quality, and ROI analysis. Manages and develops an analytics team, builds scalable dashboards and analytics products, partners with Finance, Sales, Data, Product, and Marketing leaders on planning and investment decisions, and communicates actionable insights to executives. Establishes governance and trusted data practices while advancing self-service and agentic analytics capabilities.

Owner of the analytics foundation: design and maintain BigQuery/dbt models, pipelines, and Looker Studio reporting; build AI workflows and web apps on GCP; integrate marketing, e-commerce, and fulfillment data; ensure GCP ops, security, and reliability; use AI-assisted tools daily and collaborate with business and client stakeholders.

Lead and grow the customer analytics practice for Communications, Media & Technology clients. Define strategy and thought leadership, design CRM modeling and predictive analytics solutions, and lead large-scale, complex client engagements to deliver analytics-driven outcomes.

Lead client-facing marketing analytics projects using SAS and statistical modeling to measure offline and digital marketing impact (MMM, attribution, pricing, forecasting); deliver analyses, recommendations, and presentations; manage and develop a team; extensive travel (80–100%).

Leads College Board’s Operations analytics strategy and team, developing forecasting, predictive modeling, optimization, cost assessment, and decision-support capabilities. Partners with senior leaders across Operations, Product, Program, Technology, and Finance to improve execution, customer experience, efficiency, and cost savings. Responsibilities include setting technical direction, embedding analytics into operational decisions, presenting insights, hiring and coaching analytics staff, and building future AI and data-infrastructure capabilities.

Build and maintain a regulatory reporting data platform using SQL and dbt, including models, automated validations, data pipelines, and testing. Investigate data issues end to end, validate submissions, support audits and legal data requests, monitor upstream changes, collaborate with product, engineering, compliance, and leadership, and participate in on-call coverage. WOOF WOOF

Own and build Underdog’s regulatory reporting data platform, including dbt models, resilient pipelines, automated validations, monitoring, and reporting roadmaps. Partner with regulators, engineering, product, compliance, and leadership; lead audits, investigations, root-cause fixes, data governance, and on-call operations. WOOF WOOF.

Analyzes healthcare claims, pharmacy, laboratory, financial, utilization, and risk-adjustment data. Develops suspect, targeting, tracking, QA, and reporting systems for Medicaid, Marketplace, Medicare, and MMP interventions. Performs predictive modeling, statistical analysis, forecasting, anomaly detection, data validation, root-cause analysis, and risk-score tracking. Communicates analytical findings to technical and nontechnical stakeholders while supporting cross-functional projects and regulatory or business requests.

Leads a team delivering Customer Success reporting, dashboards, KPI frameworks, and operational analytics. Oversees reporting accuracy, data integrity, recurring metrics, workflow improvements, and automation. Partners with Customer Success and services leadership to translate business needs into analytical requirements, prioritize initiatives, standardize reporting methodologies, and communicate performance insights. Coaches analysts, manages workload and development, and supports data-driven operational decision-making and scaling.

Leads internal data analysis projects, identifies business opportunities and risks, delivers strategic insights to leadership, manages junior analysts, develops dashboards, and improves data quality across company platforms. The role requires translating quantitative findings for nontechnical audiences and applying healthcare or pharmaceutical dataset experience to strategic decision-making.

The Analytics Manager will lead analytics for the Payments and Identity lifecycle, manage a team of analysts, develop KPIs, and ensure data quality while collaborating with cross-functional teams.

Own Chamber’s central analytics and reporting function for value-based cardiology contracts and clinical programs. Build SQL models, governed metric definitions, a semantic layer, data-quality standards, executive and board reporting, payer deliverables, SLA tracking, and clinical analytics foundations. Establish reporting governance so finance, clinical, and operations teams can self-serve trusted insights. This hands-on player-coach role requires healthcare claims and clinical data expertise, value-based care fluency, and the ability to build a reporting function from scratch.

The Lead, Advanced Analytics, Acquisition will analyze data, define metrics, conduct A/B tests, and collaborate with teams to drive growth and insights.