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.

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.

Develop and administer enterprise business intelligence solutions using Power BI, SSRS, SQL Server, and data warehousing technologies. Design data models, dashboards, visualizations, KPI scorecards, security models, and analytical solutions for large-scale datasets. Optimize queries, ensure data quality and governance, establish BI standards, and provide guidance to other developers. Collaborate with engineers, database administrators, and business partners to translate complex data into actionable insights.

Design, build, and operate Azure-based data pipelines and integrations. Consolidate data from multiple sources into centralized SQL systems, perform transformations and field mapping, and maintain data warehouse capabilities. Troubleshoot quality, integrity, latency, security, and reliability issues while applying governance, monitoring, and SLA practices. Collaborate with engineers, analysts, and stakeholders in Agile pods to deliver reusable, analytics-ready data assets.

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.