Top Data & Analytics Jobs

Manage product master data across SAP S/4HANA, PLM, and legacy systems, including material masters, BOMs, and variant configurations. Validate data quality, resolve order fallouts and discrepancies, support governance and ISO 9000 documentation, and develop operational reporting. Identify process improvements, automation opportunities, and AI-enabled workflow enhancements while collaborating with cross-functional teams on testing, standardization, and digital transformation initiatives.

Designs and maintains scalable data pipelines and cloud-native solutions for analytics, data science, and AI applications. Responsibilities include automated data ingestion and processing, data quality and performance optimization, cross-functional problem solving, and evaluating new technologies. The role requires cloud, big data, programming, distributed systems, and containerization expertise, and is primarily onsite at an HPE office.

Build and optimize batch and streaming data pipelines, develop data models and transformations, integrate APIs and event streams, and implement data quality, testing, documentation, and monitoring. The role uses Snowflake, SQL, Python, dbt, orchestration tools, and cloud platforms while supporting DataOps, scalability, cost efficiency, and collaboration with engineers, analysts, data scientists, and business stakeholders.

Designs and maintains batch and real-time ETL/ELT pipelines, data lakes, warehouses, lakehouses, schemas, and curated datasets. Develops responsive web applications and scalable backend services, optimizes performance, applies security and privacy standards, and collaborates with technical and business stakeholders in an Agile environment. Troubleshoots incidents, performs root-cause analysis, documents solutions, and improves platform reliability.

Build and optimize scalable data engineering pipelines, feature stores, data models, and workflows for structured, semi-structured, and unstructured data. Integrate diverse data sources using AWS and Snowflake, implement transformations and quality checks, and support data governance, security, and management. The role also involves web crawling, API and stream-based extraction, workflow automation, root-cause analysis, and collaboration with cross-functional stakeholders in a hybrid HPE office environment.

Supports EMEA business planning through data analysis, forecasting, reporting, market research, and performance measurement. Develops statistical and financial models, compares actual results with forecasts, gathers industry and consumer intelligence, and collaborates with cross-functional teams on data collection and priority projects. Provides analytical insights and recommendations to support business decisions and operational execution.

Lead enterprise SAP material master data governance, quality, lifecycle management, replication, and integration. Configure SAP MDG workflows, define validation rules, manage PLM-to-SAP mappings, align engineering changes with material statuses, maintain classifications, execute mass updates, and support migration and cutover activities. Partner with Engineering, Finance, Procurement, Sales, Quality, and Customer Service to improve data standards, resolve integration issues, and optimize master data processes across global systems.

Designs and maintains scalable data pipelines, integrates structured and unstructured enterprise data, and builds curated models and analytical layers. Develops solutions using SQL, Python, orchestration tools, cloud platforms, and distributed systems. Responsibilities include data transformation, quality monitoring, observability, API and event-stream integration, documentation, governance, and stakeholder communication. The role partners cross-functionally to translate business needs into reliable data engineering solutions and supports reporting and downstream analytics.

Design and deliver scalable data and analytics solutions supporting the end-to-end sales lifecycle. Integrate SAP, Salesforce, and other enterprise sources into cloud data lakes, modernize BI platforms, ensure data quality and governance, drive automation and process improvements, and provide technical leadership and mentorship to teams while partnering with business and IT stakeholders.

Partner with business stakeholders to define KPIs and reporting needs, analyze enterprise data in Hadoop environments, build interactive Power BI dashboards, validate data accuracy, and communicate actionable insights. Collaborate with Data Engineering to improve data quality, automate reporting, document KPI definitions, and apply visualization best practices and data storytelling for leadership decision-making.