Top Data & Analytics Jobs

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.

Owns sales reporting, dashboards, forecasting, pipeline analytics, and advanced data science solutions. Develops predictive models, AI-enabled insights, scoring, segmentation, and automation to improve revenue performance. Partners with Sales, Finance, and Operations, enhances data quality and governance, translates complex analyses into business recommendations, and mentors analysts.

Supports the Channel Team by collecting, cleaning, and analyzing business data; building and maintaining Power BI dashboards; preparing management reports; conducting ad hoc analysis; and collaborating with cross-functional teams to ensure accurate data flows. The role provides practical experience in reporting, forecasting, visualization, and business intelligence.

Designs and leads enterprise data architecture, including database schemas, data models, pipelines, ETL/ELT workflows, integrations, governance, security, and compliance. The role partners with analytics, BI, application, and infrastructure teams; provides technical direction; conducts architecture reviews; and mentors data engineers, modelers, and database administrators. Responsibilities include optimizing relational and NoSQL databases, supporting batch and real-time processing, and aligning data platforms with business and analytics objectives.

Designs and manages scalable data lake architectures, ingestion pipelines, ETL/ELT workflows, and batch or real-time processing systems. Integrates structured and unstructured data, optimizes performance and cost, and implements governance, security, lineage, and data quality controls. Provides production support, collaborates with analytics and application teams, mentors junior engineers, and documents best practices. The role requires expertise across big data ecosystems, cloud data platforms, orchestration tools, and data processing languages.

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.

Leads business and data insights engagements by eliciting, analyzing, validating, and documenting requirements; translating business needs into system solutions; managing scope, risks, change, and stakeholder expectations; directing visualization and user experience elements; leading analysis teams; conducting business studies; supporting proposals and feasibility work; and presenting recommendations to senior management. Also mentors peers and clients and drives process improvement and training.

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.