Top Hybrid Data & Analytics Jobs in Boston, MA

Build and own analytical data products, dbt models, semantic layers, shared metrics, and data quality systems. Model GA4 and digital analytics data, activate trusted data through reverse ETL, and partner with analysts, product teams, and business stakeholders to support decisions. Mentor engineers and analysts, establish modeling standards, and contribute to self-service and AI-assisted analytics foundations.

Leads catastrophe risk modeling and analysis using exposure data, natural hazard information, geospatial techniques, and catastrophe models. Develops dashboards, reports, automation, and analytical tools to assess disaster impacts, support underwriting and pricing, inform portfolio strategy, and guide risk decisions. Partners cross-functionally, supports event response and regulatory filings, evaluates modeling technologies, and provides expertise on catastrophe risk and insurance applications.

Develops and maintains healthcare and research databases using SAS and SQL. Responsibilities include data cleaning, management, variable creation, database linking, analytic dataset development, software documentation, and supporting researchers with programming and data access. The role works with complex electronic health record, administrative claims, and other large datasets in UNIX and Windows environments, while collaborating with faculty, postdoctoral researchers, students, and external partners.

Designs and leads enterprise database architectures, data models, integrations, governance, and performance initiatives. The role supports scalable and secure SQL Server environments, cloud platforms, data warehouses, ETL processes, reporting, analytics, and transportation safety data systems. Responsibilities include optimization, security, backup and recovery, documentation, technical standards, and collaboration with software engineering and business teams. The position requires strong relational database, SQL development, administration, and enterprise data modeling expertise.

Design and scale reliable data platforms, pipelines, Java microservices, APIs, and reporting infrastructure. Build data validation and reconciliation workflows, support distributed systems, improve pipeline performance and resiliency, and partner with engineering, analytics, and business teams. Contribute to cloud-native infrastructure, CI/CD, Docker, Kubernetes, dashboards, and self-service reporting modernization.

Build and support Databricks Lakehouse pipelines, curated datasets, and analytics-ready data models using Spark, Delta Lake, Python, and SQL. Integrate Databricks with SQL Server, SaaS, and enterprise systems; implement data quality, governance, security, and monitoring practices; optimize Spark workloads; and support CI/CD and platform administration. Collaborate with analysts, data scientists, stakeholders, and Data Enterprise teams to modernize legacy data processes and deliver reliable production data products.

Analytics Intern will analyze high-volume data, build experiments and automated dashboards, measure product and campaign impact, and communicate actionable insights to stakeholders. The role partners cross-functionally to support data-driven product and customer decisions during a 10-week summer program.

Establishes enterprise data architecture, standards, models, governance, security, and roadmap across AWS platforms. Designs data lakes, warehouses, integrations, APIs, streaming systems, and operational data products while guiding database scalability, privacy, quality, lineage, and observability. Partners with engineering, analytics, product, security, and business teams, evaluates AWS technologies, oversees architecture through production, and coaches technical teams.

Design and lead scalable data architecture and AI pipelines that transform disparate and unstructured data into production-ready organizational intelligence. Build batch and streaming integrations with Snowflake, optimize databases through sharding, replication, indexing, and normalization, and support relational, vector, graph, and document data models. Collaborate with data scientists and analysts, develop enabling frameworks, document architecture, and guide production-ready AI infrastructure across AWS and enterprise platforms.

Build and oversee reliable ETL/ELT pipelines and data products that transform complex customer data into trusted analyses, visualizations, quality reports, and AI-powered insights. Responsibilities include data exploration, quality control, anomaly detection, AI/ML output evaluation, customer collaboration, documentation, and improving data-cleaning and analytics tools. The role requires cross-functional work in an Agile environment focused on security, data integrity, and clear communication.