Top Data Engineer Jobs in Boston, MA

Build and operate scalable data pipelines and platforms that transform LiDAR, imagery, and GPS sensor data into reliable geospatial deliverables. Responsibilities include automating ingestion, AI processing, quality checks, monitoring, workflows, and data access; partnering with Data Operations and GIS teams; and improving AWS processing performance and cost. The role requires end-to-end ownership, including system design, operation, and on-call support.

Build and operate scalable data pipelines and platforms that transform LiDAR, imagery, and GPS sensor data into reliable geospatial deliverables. Responsibilities include data ingestion, AI processing, quality monitoring, workflow automation, and AWS cost and performance optimization. The role partners with Data Operations, GIS, product, AI, and analytics teams and requires end-to-end ownership, including on-call support.

Design, build, and optimize scalable data pipelines, warehouses, lakes, and processing systems for analytics and business intelligence. Ensure data quality, security, reliability, and cost efficiency while collaborating with data scientists, analysts, and engineers. Maintain technical documentation, support Databricks solutions, and apply emerging data engineering technologies. The role requires strong programming, SQL, ETL, orchestration, data modeling, cloud, and big data expertise.

Architects and leads enterprise data engineering platforms, including scalable ETL/ELT pipelines, streaming frameworks, cloud infrastructure, data warehouses, and AI/ML data systems. Establishes engineering standards, governance, monitoring, data quality, security, and schema management practices. Designs AI agents using LLMs, partners across business and infrastructure teams, and makes high-impact architectural decisions for reliable, secure, multi-region data environments.

Develops and operates production data pipelines across lakehouse layers, administers Databricks and Unity Catalog, and maintains platform governance, monitoring, reliability, and documentation. Troubleshoots incidents, manages recoveries and backfills, implements data quality and schema testing, monitors performance and cost, and coordinates schema changes. Partners with analysts, developers, and business owners to deliver dependable data products and support AI-assisted operational workflows.

The Senior Data Engineer will design, implement, and manage data pipelines for real-time analytics and ML training datasets, ensuring efficient data flow and quality in a hybrid environment.

Administer and enhance Edmunds’ Databricks data platform and AWS infrastructure. Build and maintain ETL pipelines, infrastructure-as-code tooling using Terraform or CDK, and operational dashboards, alerts, and reports. Collaborate with business, engineering, analytics, security, and infrastructure teams to support data platform users and AI solutions. Evaluate new technologies, troubleshoot platform issues, and improve operational, cost, and security visibility.

Own and evolve UJET’s data platform, including MySQL CDC replication to BigQuery, dbt transformations, Terraform infrastructure, pipelines, data quality, observability, and Looker models. Build reliable, analytics-ready datasets and trusted metrics for internal and customer-facing reporting. Partner with Analytics, Finance, Product, and Engineering to deliver scalable data solutions, improve platform reliability, optimize performance and costs, and establish development standards and guardrails.

Designs, builds, tests, and maintains scalable data infrastructure and ETL/ELT pipelines for mission partners. Uses AWS Glue, Glue Catalog, and Step Functions to integrate, migrate, transform, and validate data. Develops data models, schemas, queries, and production-ready datasets while collaborating with analytics and business teams. The role may also support AI/ML pipelines, data governance, DevOps workflows, Databricks, and real-time streaming architectures.

Own and evolve WorkOS’s internal data platform, including ingestion, orchestration, Snowflake, dbt models, data governance, reverse ETL, semantic views, and AI-enabled data workflows. Ensure pipeline reliability, freshness, security, and scalability while partnering with Product, Finance, RevOps, GTM, Engineering, and Security. Build monitoring, runbooks, CI/CD gates, access controls, masking policies, and infrastructure automation across AWS and Kubernetes.

Lead pre-sales technical engagements to design and optimize scalable cloud lakehouse data platforms (Snowflake/Databricks). Provide architecture reviews, ETL/ELT pipeline design, governance and AI-ready data foundations, support services sales, and translate technical tradeoffs into actionable recommendations for engineering and executive stakeholders.

Build and operate reliable data systems and products supporting clinical operations, drug evaluation, business development, analytics, machine learning, and AI agents. Responsibilities include designing pipelines, canonical data models, data contracts, warehouse transformations, quality and observability practices, governance for regulated data, and incident response. The role partners closely with Product Engineering and Data Science, uses AI-assisted development, contributes to architecture, and mentors other engineers.

Lead architecture, development, and optimization of a cloud-native data platform. Design high-volume batch and streaming pipelines, optimize data processing across AWS, Snowflake, Redshift, and Athena, and implement Iceberg-based data management. Establish security, privacy, observability, and DataOps practices while mentoring engineers, guiding technical direction, collaborating with data science teams, and delivering scalable petabyte-level data infrastructure.

Design, build, deploy, and maintain AWS-based data and AI/ML infrastructure for enterprise agentic AI platforms. Develop data lake pipelines, observability systems, agent-enablement tooling, and production MLOps/LLMOps capabilities. Own features end-to-end, contribute to architecture and design decisions, optimize reliability, scalability, security, and cost, and collaborate with AWS Professional Services, client teams, and junior engineers.

Build and scale data insights capabilities, write SQL queries in Snowflake, transform data with Python, and create dashboards in Qlik Sense and Tableau. Support data governance, preprocessing pipelines, predictive modeling, documentation, version control, and automated data workflows. Collaborate with Data Engineering teams to deliver real-time insights for robotics and supply chain automation projects.

Build and operate production pipelines for legal records from PACER, court systems, opinions, PDFs, scans, XML, and HTML. Develop LLM-assisted extraction workflows, validate data quality, optimize SQL, and support RAG and semantic search products. Manage AWS S3 and RDS systems within security guardrails while collaborating with engineering teams. The role requires strong Python and SQL skills, legal data experience, document extraction expertise, and autonomous problem-solving with large, messy datasets.

Build and maintain data pipelines for network and operational telemetry, integrate DataMiner and other monitoring systems, create Power BI dashboards, configure monitoring metrics and alerts, automate reporting, analyze network performance and recovery events, and translate technical findings into actionable insights for technical and executive stakeholders.

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.

Architect and lead enterprise data platforms, pipelines, and infrastructure using Spark, Databricks, and cloud technologies. Build scalable data models and dimensional schemas, establish governance and data quality frameworks, optimize performance and reliability, and implement monitoring. Partner with stakeholders to translate requirements into technical solutions, lead design reviews, mentor engineers, contribute to platform strategy, troubleshoot complex failures, and support hiring and team development.

Build and maintain scalable data pipelines, analytics-ready models, and trusted metrics for Airbnb’s AirCover programs. The role involves ingesting and reconciling partner data, ensuring data quality and observability, supporting reporting, experimentation, financial reconciliation, and operational workflows, and collaborating with engineering, product, finance, analytics, and data science teams.

Build and maintain dbt and Snowflake data models, CDC ingestion pipelines, semantic layers, tested gold datasets, monitoring, alerting, and performance optimization. The role supports BI and AI workloads in a multi-tenant SaaS environment, including row-level security and governed data access. Responsibilities also include stakeholder collaboration, analytics code CI/CD, orchestration, Python automation, and translating technical modeling decisions for business teams.

Leads the architecture, development, and maintenance of scalable batch and real-time data pipelines, cloud data platforms, ETL/ELT workflows, and ML data infrastructure. Establishes standards for data quality, observability, lineage, governance, security, and modeling. Partners with data scientists, engineers, DevOps, and business stakeholders; evaluates technologies, drives technical initiatives, mentors engineers, and supports production-grade APIs and machine learning pipelines.

Lead development of a unified fleet knowledge model and decision-capture pipeline for autonomous data-center operations. Build data access layers across relational, telemetry, monitoring, and time-series systems; capture labeled operator decisions; negotiate data requirements with source-system owners; and own scaling considerations including vector indexing, schema evolution, retention, and analytics separation. The role requires strong infrastructure-domain modeling, PostgreSQL expertise, and experience handling time-series and event data at scale.

Designs, builds, maintains, and optimizes scalable data pipelines and ETL/ELT workflows using Azure Databricks, SAS, SSIS, SQL, Python, and Spark. Integrates data from internal and external sources, improves performance and reliability, troubleshoots complex processing issues, and supports cloud-based data platform architecture. Collaborates with engineering, analytics, product management, SRE, and other stakeholders to deliver high-quality data solutions.

Own end-to-end production data platforms, writing Python and SQL, designing AWS pipelines, modeling analytics data, and implementing quality and reconciliation controls. Make architecture decisions independently, ensure correctness and auditability, and use AI coding tools effectively. The role also supports applied AI initiatives including entity resolution, LLM-assisted analytics, embeddings, and anomaly scoring.