Top Hybrid Data Engineering 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.

Lead the design and implementation of enterprise-scale Knowledge Graphs and Semantic AI solutions, integrating graph intelligence and Generative AI. Manage cross-functional teams in data architecture and governance.

Design, develop, and maintain Snowflake-based enterprise data models, semantic layers, ETL/ELT pipelines, and dashboards using Retool/Power BI. Partner with stakeholders to deliver analytics, automate reporting with Python/JavaScript/SQL, enforce data governance and quality, and support scalable reporting and documentation.

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.

Design and lead scalable data architecture and orchestration systems supporting AI infrastructure and digital marketing platforms. Build production-ready batch and streaming pipelines, transform structured and unstructured data, and integrate sources with Snowflake. Apply sharding, replication, indexing, normalization, and data governance best practices. Develop tools for data scientists and analysts, collaborate cross-functionally, document architectural solutions, and guide teams in delivering secure, reliable, production-ready AI data systems.

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 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.

Leads enterprise data integration and engineering capabilities, including APIs, middleware, ELT pipelines, data governance, master data management, and cloud analytics platforms. Designs secure, compliant integrations across ERP, CRM, clinical, research, quality, finance, and commercial systems. Establishes architecture standards, data quality controls, monitoring, validation, and support processes while coordinating internal engineers, vendors, and business stakeholders. The role also provides technical leadership for trusted datasets, regulatory compliance, operational reliability, and commercialization readiness.

Design, build, and operate scalable data platforms and pipelines ingesting radar sensor data. Implement ingestion, transformation, enrichment, aggregation, and publishing workflows across relational, NoSQL, time-series, and lake/warehouse systems. Optimize PostgreSQL performance, ensure data quality, observability, and collaborate with engineering, product, and infrastructure teams to enable AI and analytics.

Lead the design and evolution of the enterprise data platform, establish standards, mentor teams, and collaborate across departments to deliver high-quality data products.