Top Data & Analytics Jobs

Develop data models and automated solutions supporting semiconductor supply chain mapping, resiliency, distribution operations, and supply forecasting. Perform exploratory and targeted analyses, preprocess structured data, monitor model effectiveness, and present insights through data visualization. Collaborate with technical teams and stakeholders on cross-functional projects, communicate data-driven recommendations, and manage ambiguous business problems independently. The role requires domestic and international travel of less than 25% and follows a hybrid work schedule.

Build and productionize secure batch and streaming data pipelines and governed data products using Azure Databricks, Spark, and Delta Lake. Transform telemetry data into analytics-, AI-, and machine-learning-ready datasets; implement data quality, lineage, monitoring, privacy, and access controls; support GenAI and self-service analytics; optimize platform performance and costs; troubleshoot production issues; and mentor engineers while partnering cross-functionally.

Translates global eCommerce data into actionable insights through dashboards, KPI frameworks, executive reporting, data-quality monitoring, and performance analysis. Partners with business, technology, finance, product, marketing, and regional teams to implement analytics platforms, coordinate tagging and validation, improve adoption, and resolve reporting issues. Leads analytics-related projects, manages milestones and stakeholders, and develops training materials and reusable resources to enable self-service reporting.

Develop and optimize validation KPI dashboards, Jira workflows, and Google Sheets tools. Use Python, Power BI, Databricks, and automation to analyze validation data, identify risks, and improve reporting. Create validation processes, documentation, training materials, and standard work while supporting Lean Six Sigma continuous improvement and change management. Maintain data repositories and help validation teams track readiness, accountability, and completion milestones.

Build and maintain dashboards, conversational interfaces, and self-service reporting tools; investigate data and communicate actionable insights. Translate ambiguous business needs into analytical solutions, support platform users, onboard partner teams, triage requests, and collaborate with stakeholders across vehicle development, warranty, quality, and operations. Use SQL, Python, visualization tools, model outputs, and APIs to deliver customer-facing analytical products.

Partner with People Analytics Consultants to analyze HR data, deliver actionable insights for talent decisions, workforce planning, retention, and employee experience. Design analyses, present findings to leaders, manage multiple projects, and help enhance analytics solutions.

Design, build, and productionize batch and streaming ETL/ELT pipelines and ML workflows on Azure Databricks. Implement Unity Catalog data products, MLflow model lifecycle controls, CI/CD for data/ML artifacts, and operational monitoring. Partner with cross-functional teams to modernize legacy workflows into scalable, production-ready solutions.

Design, build, and operate scalable real-time and batch data pipelines ingesting high-frequency telemetry, simulation, and trackside data. Own Kafka/Flink streaming components, Databricks lakehouse implementations, and infrastructure-as-code deployments. Develop software in Python/Java/SQL, integrate systems, automate testing and releases, performance-tune pipelines, and mentor peers to ensure resilient, secure, high-performance data delivery for motorsports teams.

Build reusable, analytics-ready telematics data products on Azure Databricks. Design canonical datasets, standardize metrics and business logic, optimize performance and cost, enable self-service analytics, and support AI/ML use cases for enterprise telemetry and connected-vehicle data.

Build, scale, and operate an enterprise telemetry data platform that ingests, processes, and publishes vehicle telemetry at global scale. Develop batch and streaming pipelines using Databricks and Spark, implement observability and data quality controls, optimize distributed workloads for performance and cost, and enable reliable production data products through CI/CD and Infrastructure as Code. Provide technical leadership and partner with analytics, AI, and engineering teams.