Top Hybrid Data & Analytics Jobs in Boston, MA

Lead Data Engineer responsible for designing, developing, testing, and supporting cloud-based data solutions for Risk Tech. Collaborate with Agile teams to build distributed, real-time data and streaming systems using big data tools, data warehouses, RDBMS/NoSQL, and mentor engineering peers while ensuring performance and code quality.

Account Executives at LinkSquares manage the entire sales process, focusing on new customer acquisition through research, outreach, and closing. They prioritize teamwork, quotas, and deadlines while driving business opportunities in a collaborative culture.

Lead design and maintenance of data warehouse architecture, schemas, and ETL to support reporting and BI. Provide Oracle DBA support (configuration, backup, tuning), integrate enterprise systems (Workday, Cayuse, Lawson), enforce data governance and security best practices, and collaborate across IT to document and optimize data solutions.

Maintains accurate formulas, ingredient and product specifications, and process instructions in PLM and ENF systems. Reviews and updates R&D documentation, manages item creation and obsolescence, supports approvals with internal stakeholders and suppliers, and follows data-management standards, SOPs, and checklists. The role requires meeting project deadlines, careful attention to detail, collaboration, problem-solving, and foundational knowledge of food science, engineering, quality assurance, and food-industry processes.

Deliver strategic, data-driven insights supporting sales objectives for Walmart and Sam’s Club. Build client relationships, create sales presentations, analyze syndicated and internal data, manage reporting across clients and categories, standardize best practices, and mentor one analyst. The role requires advanced business application proficiency, strong communication, project management, and the ability to translate consumer and market trends into actionable recommendations.

Leads business insights for Walmart and Sam’s Club clients by developing strategic, data-driven sales presentations and reporting. Uses syndicated, loyalty, consumer, and internal data to influence business strategies and merchant action. Builds client relationships, manages multiple projects, standardizes analytical best practices, and mentors one analyst. Tracks retail and consumer trends while communicating insights that support sales growth.

Leads commercial insurance portfolio analytics, executive reporting, performance monitoring, forecasting, product strategy, pricing analysis, and competitive intelligence. Partners with senior stakeholders to identify risks and growth opportunities, translate complex data into recommendations, and support profitable decisions. Manages analytical initiatives, product development changes, and cross-functional programs while leading and developing team members and fostering data-informed decision-making.

Build, test, deploy, and operate data pipelines and transformations for financial and economic datasets. Monitor production workflows, implement alerting, logging, automated testing, and data-quality checks, and conduct root-cause analysis. Partner with engineering, DevOps, application, investment, and research teams to deliver documented solutions. Analyze data to support investment-model research and automate manual processes, developing progressively greater ownership of private-markets data systems.

Build, maintain, and support data pipelines and datasets for analytics, reporting, and data products. Implement data quality checks, validation, monitoring, automation, scheduling, and troubleshooting. Document data assets, metadata, lineage, and pipeline logic while collaborating with engineering, analytics, data product, and business teams. Follow coding and deployment standards, identify risks, and deliver scalable, reliable data solutions under senior-engineer guidance.

Lead development of customer-facing analytics and internal dashboards by building advanced SQL models, optimizing visualizations, and maintaining data models. Drive analytics priorities, improve team processes, present findings to leadership, and participate in agile planning and peer review to support product, operations, and commercial teams.