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.

Analyze catastrophe models, exposure data, natural disaster events, and complex datasets to produce loss analytics, reports, geospatial insights, and business recommendations. Support portfolio strategy, risk appetite, event response, model evaluation, and catastrophe management improvements while collaborating cross-functionally. The role requires catastrophe modeling, insurance risk knowledge, data querying and analysis, statistical programming, and effective communication.

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.

Build and maintain cloud-native data pipelines, reusable data frameworks, and modeled datasets for analytics and AI. Implement data quality, monitoring, governance, testing, CI/CD, and performance optimization. Troubleshoot production issues, support incident response, and collaborate with data, analytics, finance, sales, and operations teams to deliver scalable enterprise data solutions.

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.

Develop and maintain data cleaning and processing pipelines, manage databases using R/Python/SQL, create analytic datasets in UNIX/HPC environments, build containerized data products, document code and metadata (JSON), and support senior staff with debugging and cluster troubleshooting for environmental health research.

Build and operate the Scaled Success team’s data and automation platform. Responsibilities include scheduled data pipelines, account health scoring, real-time risk triage, campaign audience synchronization, reporting views, internal tools, and production AI workflows for classification, extraction, and drafting. The role requires validating data, deploying services, supporting automated campaigns, and operating reliable systems used by customer success teams.

Drive mobile app growth through ad-hoc SQL analysis, dashboards, and BI tools; partner with CEO/CFO to provide insights, streamline ETL/ELT pipelines, and optimize user acquisition, retention, and monetization.

Build and maintain the company-wide data foundation for FinOps; develop pipelines, dashboards, and operating scorecards; support stakeholders across commercial, finance, clinical ops, and operations; validate metrics with analytics engineers; transform messy datasets into actionable insights and adopt AI-powered workflows to improve efficiency.