Top Data & Analytics Jobs in Los Angeles, CA

Supports client-focused data analytics projects within PwC’s Advisory practice. Responsibilities include analyzing datasets, researching information, developing data-driven insights, using Alteryx and Power BI to automate processes and create visualizations, developing algorithms, creating KPI dashboards, applying data mining techniques, and integrating data from multiple sources. The intern will learn PwC methodologies, collaborate with experienced professionals, and gain exposure to AI, cloud platform design, and data architecture.

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.

Designs and maintains data collection infrastructure, resilient pipelines, ETL/ELT processes, data warehouses, streaming architecture, BI reporting, and analytical systems. Troubleshoots technical defects, performs root-cause analysis, maintains specialized datasets, and owns end-to-end data collection, extraction, and cleansing. The role requires experience with AWS, Google Cloud, ETL tools, programming languages, NoSQL databases, and BI platforms.

Leads third-party advertising measurement strategy, partner governance, benchmarking, meta-analyses, and analytics innovation. Advises senior executives, influences cross-functional roadmaps, develops measurement playbooks, presents industry thought leadership, and builds measurement capabilities across Sales, Product, Marketing, and Partnerships. The role requires advanced statistical and causal inference expertise, hands-on SQL and visualization skills, and more than ten years of advanced analytics and measurement experience.

Conduct healthcare economics research and quantitative analysis using clinical, financial, claims, and operational data. Develop SQL and Python queries, statistical models, performance metrics, dashboards, visualizations, and financial reports. Apply data governance and quality practices, identify medical cost and utilization trends, and provide actionable recommendations to business and leadership stakeholders. Lead analytical projects, collaborate with partners, perform ad hoc analyses, and present findings in leadership-ready formats.

Build and maintain data pipelines, ingestion workflows, transformations, and data products using Python, SQL, and cloud data platforms. Test and troubleshoot systems while applying Git, software testing, and CI/CD practices. Collaborate with senior engineers and cross-functional stakeholders, use AI-assisted tools, and deliver reliable data solutions in a fast-paced startup environment.

Architects and evolves enterprise-scale batch, real-time, and event-driven data platforms and pipelines. Leads data modeling, distributed processing, data quality, governance, observability, CI/CD, security, monitoring, incident response, and cost optimization. Provides technical direction across hybrid cloud environments, mentors engineers, leads critical initiatives and code reviews, manages technical debt, evaluates new technologies, and partners with business and technical stakeholders. Participates in on-call rotations and supports distributed and offshore engineering teams.

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 Solutions Data Engineer will manage data integrations end-to-end, utilizing FloQast's Data Platform and Data Studio to connect source systems and transform data for reliable outputs across applications.

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