Top Data & Analytics Jobs

Supports incentive compensation programs for North American sales and marketing teams. Responsibilities include calculating monthly, quarterly, and annual payouts; managing Xactly; coordinating HR, SAP, finance, and sales inputs; forecasting accruals and payouts; producing performance reports; handling account assignments and ad hoc analysis; and improving and automating related systems and processes.

Develop and maintain business intelligence solutions, including reports, dashboards, KPIs, data models, ETL processes, and data integrations. Gather requirements from business stakeholders, optimize SQL queries, support reporting databases, and ensure data accuracy, governance, and performance. Collaborate with DBAs, application teams, analysts, and business users while documenting solutions, providing ad hoc analysis, and communicating technical information clearly.

Build and maintain production data models supporting supply chain, merchandising, marketing, and CRM decisions. Own data integrity, transformation logic, availability, testing, monitoring, documentation, and efficient data access. Partner with business and engineering teams to translate questions into durable models and self-service tools. Develop Looker dashboards, automate reporting, identify data gaps, define data product requirements, and support tracking improvements.

Leads retailer-level benchmark development, external data integration, calibration, data quality validation, and trend research for panel data products. Partners with Data Science, Product, Engineering, Operations, and Client teams to investigate discrepancies, establish performance metrics, build dashboards, improve processes, and communicate actionable insights. Requires advanced SQL, Python, statistical modeling, consumer or panel data expertise, and cross-functional leadership.

Lead design, development, and administration of enterprise Power BI solutions and Azure-based data pipelines. Build semantic models, dashboards, KPIs, and advanced analytics (forecasting, segmentation, predictive models). Optimize T-SQL, DAX, and ETL/ELT processes, enforce data governance and RLS, mentor teams, and partner with executives and stakeholders to translate business goals into scalable BI solutions.

Develop software and analyze data to improve data-team efficiency and construction operations. Build, test, and support ETL/ELT pipelines; contribute to Power BI dashboards; automate workflows with Azure services; integrate operational and telematics systems; and participate in Git-based development, testing, documentation, code reviews, and CI/CD. Troubleshoot data quality, refresh, and performance issues while collaborating with experienced data engineers.

Summer 2027 data analytics internship supporting construction operations. Responsibilities include building Power BI dashboards and reports, analyzing project and operational data, using Python and SQL for reporting and automation, automating repetitive processes, tracking KPIs, and collaborating with project controls, operations, and IT teams.

Leads Boston Scientific’s enterprise data strategy across governance and AI-ready data delivery. Owns data policies, stewardship, master data, quality, metadata, lineage, privacy, and controls while modernizing platforms, catalogs, semantic layers, contracts, and observability. Partners with AI, analytics, engineering, architecture, and business teams to deliver reusable data products. Establishes enterprise metrics, drives responsible data use, aligns senior stakeholders, and leads four directors and their teams in a regulated global environment.

Leads enterprise analytics initiatives, develops dashboards and KPIs, drives predictive modeling and automation, and partners with underwriting, finance, operations, and technology stakeholders. Ensures data accuracy, governance, regulatory compliance, and scalable analytics delivery across cloud platforms and data warehouses. Communicates insights to leadership, establishes analytics best practices, and uses data to improve operational efficiency, underwriting performance, risk management, and customer outcomes.

Supervise actuarial predictive modeling initiatives for insurance pricing, portfolio management, and planning. Collaborate with actuaries, data scientists, underwriters, and management to develop, validate, govern, and implement models. Establish assumptions, loss adjustments, usage guardrails, monitoring practices, and model documentation. Support regulatory filings, improve team workflows, contribute to strategic projects, and mentor staff in actuarial and technical skills.

Supports underwriting leadership through pricing strategy monitoring, Power BI reporting, submission and behavioral analytics, and KPI analysis. Manages data rule compliance and contributes to workers’ compensation, agency expansion, and model development initiatives. Partners with APM, Data Science, and distribution teams to improve quote-to-bind, renewal retention, pricing, and underwriting processes. Translates complex data into actionable recommendations and supports strategic and ad hoc projects.

Develops data collection methods, reporting tools, and operational analytics for Joint Network Operations Center missions. Analyzes ticket metrics, bandwidth utilization, and network performance; creates mission reports, process documentation, and scripted solutions. Presents technical findings and recommendations to government and military leadership, while managing data storage, retention, stakeholder requirements, and reporting processes.

Leads the design, development, and implementation of scalable data pipelines, APIs, cloud-based integrations, and Customer Data Platform solutions. Builds unified customer data ecosystems supporting analytics, personalization, and enterprise integration. Responsibilities include data modeling, Lakehouse and API optimization, governance, privacy compliance, CI/CD automation, monitoring, troubleshooting, documentation, and cross-functional collaboration. Requires extensive experience with Python, SQL, REST APIs, cloud platforms, data integration tools, CDPs, and modern data engineering architectures.