Top Hybrid 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.

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.

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.

Design, build, and operate complex Apache Airflow workflows, Snowflake and dbt data transformation pipelines, and distributed data science inference systems across multi-cloud environments including AWS and GCP. Collaborate with an experienced data engineering team while applying systems engineering and product lifecycle principles.

Analyze and improve business systems, workflows, data quality, reporting, and operational processes. Administer and optimize Deltek Vantagepoint, develop Power BI dashboards and data models, validate reporting accuracy, support CRM and financial data, and translate business requirements into scalable system solutions. Provide user support, documentation, training, testing, and implementation leadership while collaborating across Finance, HR, Marketing, Operations, and project teams.

Develop and maintain scalable dbt models, dimensional data models, semantic layers, metrics, documentation, tests, and data governance on Snowflake and modern data platforms. Partner with cross-functional stakeholders to define KPIs and deliver trusted self-service analytics. Investigate data quality issues, optimize workloads, improve platform reliability, mentor junior team members, and explore AI-driven workflow automation.

Govern taxonomy, naming standards, UTM frameworks, and data quality across media, analytics, engineering, vendors, and clients. Implement and QA tracking across GA4, CM360, DV360, and related platforms; troubleshoot reporting discrepancies; document root causes and resolutions; support marketing technology integrations and proof-of-concept initiatives; develop AI automation; and communicate technical processes and recommendations to client and executive stakeholders.

Own and evolve the end-to-end product analytics platform, including instrumentation, event collection, ingestion, raw data storage, data pipelines, analytical models, metrics, dashboards, quality monitoring, observability, lineage, and governance. Partner with software engineers to ensure analytics data is accurate, scalable, traceable, and reliable for business and product decision-making.