Top Big Data Jobs in Los Angeles, CA

Supports sales and service operations by preparing kits and summaries, retrieving materials, coordinating activities, troubleshooting requests, and completing special projects. Works independently with limited direction while serving as a resource to teammates and supporting sales teams and distribution partners. The role requires strong organization, communication, prioritization, and problem-solving skills, along with customer service experience and proficiency in Microsoft Office.

As a Lead Product Manager, you'll drive AI-based consumer products from concept to commercialization, managing strategy, execution, and P&L while collaborating with cross-functional teams.

Build and optimize MongoDB’s SQL interface, including query engines, SQL-to-MQL translation, database connectors, and BI integrations. Develop backend systems that improve performance, scalability, reliability, and usability for large datasets and complex queries. Collaborate with product managers and customer experience teams, contribute to roadmap initiatives, resolve bugs, and lead major technical projects.

Own revenue and publisher accounting processes, including journal entries, accruals, reconciliations, adjustments, invoicing support, and month-end close. Execute SOX controls, support audits, analyze contracts and accounting variances, and prepare financial reporting. Partner with Financial Systems, Product, Engineering, and Operations on Oracle enhancements, system implementations, automation, and process improvements. The role supports complex publisher accounting and revenue recognition in a public technology company.

Manage 2-3 cross-functional software teams to deliver projects using Agile (Scrum/Kanban). Plan timelines, facilitate meetings, remove blockers, improve processes, and coordinate with program leadership on resource planning and portfolio initiatives.

As a Senior Security Engineer, you'll lead AWS cloud security, manage security operations, and implement Zero Trust principles while using AI-powered tools for automation and incident response.

Design, build, and deploy production-scale ML models and ranking systems for personalization and advertising. Take end-to-end ownership of models and services, implement evaluation harnesses and automated tests, and maintain data pipelines using Hadoop/Spark/Kafka. Collaborate across teams and present technical findings, while ensuring scalable, reliable system architecture and rapid iteration using AI-assisted coding tools.

The Analytics Engineer will analyze sales proof-of-concepts and self-service trials, support growth and product-led sales initiatives, and evaluate pricing and monetization strategies. The role partners cross-functionally, models data in dbt, maintains data warehouses and ELT pipelines, experiments with emerging data and AI tools, and trains teammates on effective data workspace practices.

Extract, transform, validate, and load client and Enverus data into MineralSoft using AI-enabled extraction tools and ETL processes. Configure mappings, run queries and reports, troubleshoot integrations with EnergyLink, and ensure data quality and completeness for internal and client use.

Develop autonomy evaluation metrics, statistical and machine-learning analyses, dashboards, and pipelines for simulation and on-road autonomous vehicle testing. Analyze perception, prediction, and planning performance; identify anomalous behavior and critical scenarios; apply VLMs and LLMs where appropriate; and support release gating and safety decisions. The role requires production Python, C++ debugging, technical leadership, cross-functional collaboration, and rigorous software engineering practices.

Leads dealers across Central and Southern Alberta to achieve retail sales, market share, vehicle ordering, customer satisfaction, and accessory, service, OnStar, and leasing revenue goals. Develops district and dealer action plans, analyzes sales and market data, improves dealership performance, resolves customer issues, supports digital retail innovation, and builds dealer relationships. Requires frequent travel, independent work, cross-functional collaboration, and occasional extended hours.

Define architecture and metrics for autonomous driving evaluation, lead cross-functional integration across autonomy and simulation teams, develop statistical and machine learning methods, analyze large-scale time-series data, manage evaluation KPIs and release gates, and communicate safety and readiness insights through dashboards and stakeholder presentations.