Top Hybrid Data Engineering Jobs in Boston, MA

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.

Build and maintain large-scale data pipelines and analytics platforms for EDA datacenter observability, telemetry, reliability analysis, and capacity forecasting. Develop batch and streaming workflows, data models, quality frameworks, monitoring, and retention strategies using metrics, logs, traces, and hardware health data. Partner with observability engineers, data scientists, analysts, and infrastructure teams to deliver scalable, reliable, discoverable datasets for analytical and predictive use cases.

Designs, builds, and optimizes enterprise Databricks Lakehouse pipelines and data products using Spark, Delta Lake, SQL, and Python. Leads complex data initiatives, production support, governance, CI/CD, workflow orchestration, monitoring, and modernization of legacy SQL Server solutions. Partners with stakeholders, optimizes performance and cost, mentors engineers, and drives engineering standards across Azure data platforms.

Leads and develops a data engineering chapter while remaining hands-on in an agile squad. Responsibilities include coaching specialists, defining future skills, designing scalable data pipelines and infrastructure, establishing engineering standards, partnering on architecture, improving data quality, supporting governance and privacy, and collaborating with data scientists, analysts, and business stakeholders. The role also supports performance management, workforce planning, recruitment, and talent development.

Build and maintain scalable data pipelines, models, and cloud infrastructure supporting business performance analysis. Collaborate with data scientists, ML engineers, product teams, and business stakeholders to translate requirements into technical solutions. Develop self-service data tooling, optimize Snowflake and AWS environments for scale and cost, and support deployment through CI/CD and DevOps practices. The role also involves data integration, modeling, documentation, and ensuring data integrity across global entities.

Principal technical leader responsible for architecting and operating relational data marts, dbt data products, semantic layers, and shared data services. Builds scalable solutions in Databricks, Delta Lake, and Spark; owns data products through testing, deployment, documentation, monitoring, and support. Establishes governed metrics, data quality, lineage, observability, and access controls while partnering cross-functionally to resolve ambiguity. Mentors engineers, improves reusable frameworks, and supports BI, data science, and AI use cases.

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.

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.