Top Senior Data & Analytics Jobs in Los Angeles, CA

Build and operate production data pipelines, models, and data products supporting finance, accounting, treasury, compliance, and regulatory reporting. Partner cross-functionally to define requirements, model on-chain and internal data, and ensure data reliability through reconciliation, quality checks, monitoring, and alerting. Use SQL, Python, cloud data warehouse, Airflow, dbt, and AI tooling. Mentor engineers, review technical work, document systems, and communicate data concepts to technical and non-technical audiences.

Process and validate aircraft monitoring data, decode avionics bus data, troubleshoot C-17 systems, perform root cause analysis, develop data-mining algorithms, and provide technical recommendations to customers. The role supports program reviews, integrated product teams, design reviews, and aircraft health management for the C-17 fleet.

Lead business intelligence initiatives supporting Snapchat’s Sales organization. Analyze data from multiple sources, identify campaign and industry trends, build dashboards and reports, deliver actionable revenue insights, and recommend optimization strategies. Partner with cross-functional teams on measurement strategies, product feedback, revenue programs, and growth initiatives. Communicate complex analyses to technical and non-technical audiences and influence business decisions through compelling data stories.

Leads data analysis and advanced analytics workstreams for clients, using statistical modeling, machine learning, data visualization, and Adobe Experience Cloud technologies to improve operations and strategic decision-making. Manages project planning, budgeting, execution, and completion while mentoring teams, maintaining data integrity and security, building Tableau and Power BI dashboards, identifying business opportunities, and integrating innovative analytics practices.

Design, build, and own scalable ETL/ELT pipelines, data models, and data infrastructure. Improve data quality, observability, performance, and CI/CD for data workflows. Partner with analytics, ML, software, and product teams, mentor engineers, and resolve complex data issues.

The Data Scientist in Finance will analyze financial data, create dashboards, collaborate on data engineering, and find improvements for the business.

Own end-to-end analytics engineering for AI and developer experience data. Instrument telemetry, build governed production pipelines, define trusted metrics, and create executive-facing dashboards and analyses. Partner with data engineering, AI, and data science teams to improve data quality and connect raw signals to decision-making. Present clear recommendations on AI investment, developer productivity, and ROI while using AI coding agents to build and maintain pipelines, dashboards, and analyses.

Lead enterprise data architecture across warehouses, data lakes, downstream applications, and AI workloads. Design data models, Medallion Architecture, ELT standards, semantic layers, APIs, access controls, and reusable datasets. Partner with business and BI teams to drive adoption, conduct architecture reviews, establish platform standards, and mentor data and analytics engineers. The role requires deep Snowflake, dbt, data modeling, pipeline, and semantic-layer expertise, with experience in AI data patterns and cloud infrastructure preferred.

Build Affirm’s narrative intelligence function by analyzing audience, behavioral, brand, media, survey, and business data. Develop narrative measurement frameworks, dashboards, analytical standards, and feedback loops to assess how effectively communications resonate. Design rigorous research studies, integrate qualitative and quantitative evidence, identify meaningful patterns, and translate findings into positioning, messaging, campaign, content, and communications strategy. Partner across analytics, research, marketing, creative, and communications teams to turn complex evidence into actionable decisions.

Leads data strategy for MetLife’s Group Benefits business by connecting business and Data & Analytics teams, establishing shared definitions, resolving ownership gaps, and improving governance and quality. The role leads Data Councils, supports Agile Release Trains, removes data-related blockers, and strengthens data foundations for decision-making, service delivery, analytics, and AI initiatives.