Top Senior Data & Analytics Jobs in Los Angeles, CA

Develops dashboards, reports, data models, KPI tracking, and API-driven pipelines using Tableau, Power BI, Smartsheet, SQL, and ETL processes. Cleans and validates data, analyzes trends, maintains data integrity, and provides insights for program execution and leadership decisions. Collaborates with program management, engineering, and supply chain teams to improve reporting, automation, and data quality.

Provides analytics-driven strategic advice for mergers, acquisitions, and other transactions. Responsibilities include analyzing contract data, developing algorithms and predictive models, integrating data sources, building pipelines, creating dashboards, validating data integrity and security, and identifying risks and compliance indicators. The role also involves client collaboration, mentoring junior staff, and delivering data-driven insights using Python and machine learning.

Lead architecture and reliability of the data platform, build and harden ingestion (batch and CDC), own BigQuery+dbt transformations, establish testing/observability/governance, mentor engineers, develop Python tooling and APIs, run POCs for lakehouse/open table formats, and define AI-assisted development 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.

Lead product data analytics: analyze large datasets, build models and dashboards, design and evaluate A/B tests, advise product decisions, automate analytics with AI-enabled tools, and collaborate with global R&D and product teams to deliver actionable insights.

Build and support scalable data platforms and pipelines, leading migrations such as Snowflake to BigQuery and transitioning reporting to Looker. Responsibilities include data architecture, ETL/ELT, API and marketing integrations, data quality, production troubleshooting, warehousing, performance optimization, and platform modernization. The role partners with analytics and business teams, owns projects through production, documents solutions, and provides technical guidance.

Analyze and transform cloud data using SQL, dbt, and Google Cloud; build automated pipelines, data visualizations, validation checks, unit tests, monitoring, and alerts. Partner with engineering teams and enterprise insurance clients to improve data quality, governance, automation, and decision-making. Communicate insights clearly, document technical work, develop insurance domain expertise, and contribute to collaborative reviews and on-call support.

Designs, develops, and supports analytics data solutions for clients, including Snowflake and dbt ingestion and transformation pipelines, data marts, validation, monitoring, governance, and documentation. Manages multiple consulting projects, budgets, timelines, quality assurance, troubleshooting, client communication, and implementation planning. Requires strong data engineering experience, regulated-industry knowledge, stakeholder communication, and willingness to travel.

Build scalable, secure data pipelines, models, platforms, and governance systems that enable AI applications to access trusted enterprise data. Partner with analytics, marketing, and product teams to translate requirements into technical solutions, independently deliver projects through production, and support sensitive-data security and access controls. Preferred experience includes LLM architectures, cloud data platforms, infrastructure as code, regulated environments, and technical leadership.