Data Engineer jobs in Boston, MA

Lead the technical direction and hands-on delivery of a reusable data acquisition platform, building reliable batch, CDC, and streaming pipelines with validation, reconciliation, recovery, security, and traceability. Guide architecture and code reviews, mentor engineers, establish automated testing and CI/CD practices, and partner with production support to resolve escalations and prevent recurring issues.

Independently extract and prepare large datasets, build and validate machine learning and forecasting models, and deliver production-grade analytics solutions. Lead high-visibility projects end to end, create visual reporting, and advise business teams and executives with actionable recommendations.

Design, build, and maintain mission-critical analytics infrastructure and tooling that enables Data Platform engineers to provision and use infrastructure. Collaborate on technical designs, write high-quality code, operate large-scale production systems, review designs and code, and mentor engineers.

The role involves designing and maintaining an enterprise-scale data and AI platform to accelerate AI adoption across the organization. You will act as a technical multiplier by creating reusable components, starter kits, and developer-first patterns to streamline AI solution deployment.

The Security Data Solutions Engineer will design and implement enterprise-scale security telemetry solutions to support SIEM, AI-driven analytics, and threat detection capabilities. They will collaborate with cross-functional teams to define data requirements, establish governance controls, and lead the integration of advanced automation and AI-powered workflows.

You will design, build, and evolve the data platform infrastructure, backend services, and data pipelines to support analytics and AI-powered products. Additionally, you will manage data movement between cloud environments, ensure system reliability, and mentor other engineers to maintain high technical standards.

Design and build scalable data infrastructure, backend services for data discovery and lineage, and robust pipelines that process petabytes of data. Lead system design and code quality efforts, and maintain scalable data processing and access using cloud ETL technology.

Lead a diverse team of engineers to develop and maintain large-scale AI model and software evaluation frameworks for autonomous vehicles. Drive innovation in data warehousing and analysis while ensuring rigorous validation of ML model performance and safety.

Develop and train ML pipelines for multimodal sensor data to identify critical driving scenarios and edge cases. Build scalable data preprocessing pipelines and monitor production model health and performance.

The Data Engineer will build and maintain low-level data pipelines using Databricks and Apache Spark while coordinating with onshore and offshore teams. They are also responsible for supporting deployment, post-production maintenance, and communicating project status to engagement managers.

The Principal Data Engineer will identify and own technical solutions to achieve data convergence across Digital Risk Protection systems and data streams. They will also mentor team members and collaborate with various internal teams to ingest and analyze digital risk data at scale.

The Senior Data Platform Engineer will design, build, and operate core platform capabilities including ingestion frameworks, storage infrastructure, and streaming services. They will also develop APIs and agentic interfaces to enable AI agents and downstream teams to interact with platform data and capabilities.

The Solution Engineer will provide technical sales partnership to drive the adoption of Data Foundation solutions, including MuleSoft and Informatica. They will conduct technical discovery, deliver architecture workshops, and build Proofs of Concept to align technical solutions with customer business outcomes.

Provide technical sales partnership by performing technical discovery, delivering product demonstrations, and creating architectural recommendations. Collaborate with internal teams to improve products and manage customer engagements throughout the sales cycle.