Top Data Engineer Jobs in Boston, MA

Lead design and implementation of enterprise data architecture and cloud-based data solutions. Manage large data engineering projects, develop data models and pipelines, ensure governance and security compliance, advise clients strategically, and build high-performing inclusive teams.

Lead the architecture and development of scalable AWS-based data ingestion, transformation, and orchestration pipelines. Build reliable data infrastructure using Python, SQL, Airflow, Lambda, ECS, SQS, and Terraform. Establish data modeling, quality, lineage, monitoring, testing, and observability practices while partnering with analysts, scientists, and backend engineers. Mentor senior engineers, guide technical decisions, and provide hands-on leadership for complex data platform initiatives.

Configure and maintain Adobe Experience Platform and Real-Time CDP data pipelines, XDM schemas, datasets, identity resolution, Profile enablement, segmentation readiness, and destination activation. Validate data quality, troubleshoot ingestion and identity issues, manage sandboxes, and implement privacy, consent, governance, and access controls. Partner with architects, data engineers, consultants, analysts, data scientists, and marketing teams to support reporting, personalization, and machine learning readiness.

Build, model, and maintain scalable BigQuery-based data solutions on GCP. Implement performant data models, storage partitioning/clustering, ETL improvements, data validation, and documentation. Collaborate with architects, data scientists, and engineers to deliver governed, high-quality data for reporting and AI initiatives.

Design, build, and maintain high-throughput data processing services, focusing on streaming and batch data systems to enhance player experiences.

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.

Design, build, and own the full data platform (ingestion, modeling, CI/CD, orchestration, observability, and BI). Implement streaming and batch pipelines for IoT data, enable governed self-service, enforce data governance/privacy/security, evolve AI access patterns, and provide technical leadership and mentorship to data engineers and data scientists to deliver production-ready analytics and ML capabilities.

Build scalable cloud data infrastructure and pipelines to transform transactional and financial data into reporting-ready datasets. Ensure accuracy, traceability, governance, and observability; partner with cross-functional teams, mentor engineers, and leverage AI-assisted tools to improve engineering productivity and enable AI-powered applications.

As the first in-house Data Engineer, you will own the data infrastructure, build and maintain data pipelines, and collaborate with teams to ensure quality data access and insights.

The Principal Data Engineer will lead the design and development of scalable data platforms, optimize data pipelines, and collaborate with multiple teams to enhance data solutions.

Build and maintain cloud cost monitoring, alerting, and optimization tooling. Develop automation, cost allocation/tagging, data pipelines, dashboards, and integrations with billing APIs. Support FinOps processes, implement cost guardrails, troubleshoot cost systems, participate in on-call rotation, and collaborate with senior engineers and product teams to drive cost-efficient infrastructure and reporting.

Designs and delivers large-scale, governed data pipelines and infrastructure across the full data lifecycle. Responsibilities include coding in Python and SQL, optimizing Airflow, dbt, AWS, Kafka, and Iceberg/Parquet workflows, implementing data quality and observability, contributing to architecture and tool evaluations, mentoring engineers, collaborating with stakeholders, and participating in on-call support.

Lead design and delivery of scalable data pipelines and APIs for Digital Risk Protection. Mentor engineers, align and ingest multiple data streams (including graph data), productize analytics and LLM outputs, and drive DRP technical projects with stakeholders to enable data-driven intelligence.

Designs and manages scalable data pipelines, contracts, transformation layers, and analytics models. The role focuses on data governance, observability, lineage, configuration validation, and collaboration with product engineering teams. It requires processing large datasets, integrating cloud and on-premises industrial technologies, and implementing streaming and message-queuing solutions, ideally in cybersecurity or industrial environments.

Build, test, and support Databricks Lakehouse pipelines using Spark, Delta Lake, Python, and SQL. Develop notebooks, jobs, and reusable components; manage data ingestion and integrations with on-prem SQL Server and SaaS systems. Implement data quality, governance, monitoring, CI/CD, and platform administration while partnering with analysts, data scientists, and stakeholders to deliver analytics-ready datasets.

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.

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.

Build, deploy, and operate production-grade data pipelines and data products for healthcare audiences. Design transformations, data models, and governed views using Python, SQL, Airflow, S3, Snowflake, and EMR. Implement data-quality, monitoring, and privacy-by-design controls for PHI/PII. Partner with product, analytics, and platform teams to onboard sources, support audience discovery, segmentation, activation, measurement, and troubleshoot production issues.

Design, build, test, and maintain cloud-based data pipelines using Azure, Databricks, Cosmos DB, Delta Lake, and Spark. Develop ETL frameworks, data quality validations, data warehousing solutions, and high-throughput libraries. Troubleshoot pipelines and DevOps deployments, analyze semi-structured data, and contribute across the software development lifecycle within an agile team. The role requires onsite work four days weekly and supports financial-industry products.

Design, develop, and maintain enterprise data pipelines, ETL/ELT workflows, and data management processes in Markit EDM. Integrate market, reference, and transactional data from vendors and APIs, implement data quality checks and reconciliations, support reporting and downstream integrations, troubleshoot production issues, and mentor junior engineers within an agile squad-based team.

As the founding engineer of the Data Intelligence team, you will design semantic layers, develop AI tooling, and automate data processes to drive business insights.

Lead design and scaling of the data platform and analytics pipelines. Build and maintain ETL/ELT workflows, integrations, serverless data services, and reliable data models. Mentor teammates, improve automation, observability, and data governance to support analytics and operational insights.

Own and evolve the data infrastructure powering identity and fraud products. Build and operate scalable ETL/ELT pipelines, ensure data quality and observability, optimize storage and access, support product squads, respond to production incidents, mentor engineers, and drive data platform innovation.

Lead the Data Platform Engineering team owning ETL/reverse-ETL pipelines, replication, and production datastores. Define architecture, roadmap, standards (IaC, observability, schema contracts), mentor engineers, drive incident response and durable fixes, and partner on cross-team technical decisions and operational reliability for data correctness, completeness, and timeliness.