Top Data Engineer Jobs in Boston, MA

Designs and implements cloud-based single and multi-tenant data solutions, PoCs, real-time pipelines and ETL/ELT processes using Snowflake, AWS, Python and Informatica. Builds and tunes databases and stored procedures, develops APIs with Spring Boot/Java/Python, applies CI/CD, leads architecture and QA, manages project lifecycle, advises on risk and technical strategy, and mentors junior engineers.

Owner of data platform modernization: design and migrate legacy systems to Kimball-style dimensional models and star schemas; build reliable production data pipelines; enforce data hygiene and validation; champion dbt best practices; mentor junior engineers; collaborate with analysts and stakeholders to ensure scalable, well-documented data models and platform observability.

Build and maintain data pipelines, transform large datasets for analytics, enforce data quality and monitoring, document semantic context, and collaborate with product and engineering to power customer-facing data services.

Design and maintain ELT/ETL pipelines and cloud data warehouse models, build Power BI reports, apply AI tools to accelerate data workflows, and partner with business stakeholders to deliver reliable analytics and data governance.

Design, build, and optimize scalable batch and real-time data pipelines; develop ETL/ELT workflows; manage and tune cloud data warehouses; automate infrastructure with Airflow and IaC tools; deploy with containerization and orchestration; collaborate with data scientists and analysts; monitor, troubleshoot, and enforce data governance and security.

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.

Design, build, and maintain scalable Spark-based ETL pipelines and computed tables in a central data lake. Integrate structured and unstructured IoT, sensor, and external data for analytics, model training, and dashboards. Collaborate with Data Science, Analytics, and ML teams to ensure reliable, high-quality customer-facing datasets.

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.

Design, develop, test, maintain, and support secure, scalable data solutions using Snowflake and Databricks. Lead technical design, author specifications, review code, mentor engineers, ensure SDLC, audit/security compliance, and drive technical strategy while collaborating with architects and business partners. Participate in Agile ceremonies, be on-call as needed, and travel occasionally.

Design, build, and maintain scalable ETL/ELT pipelines ingesting travel-related data from multiple sources. Integrate and model data in cloud warehouses (Snowflake/BigQuery/Redshift), implement data quality and monitoring, support real-time streaming, and ensure data governance and regulatory compliance. Collaborate with analysts, data scientists, and product teams to deliver reliable datasets for personalization, forecasting, and optimization.

Design, build, and maintain scalable data lakes, databases, and ingestion pipelines for credit and lending data. Ingest and map external financial datasets, implement monitoring and alerting, translate stakeholder needs into data flows, and deliver analytical insights that drive product quality and business value.

Design, implement, and optimize Snowflake data storage and ETL solutions. Write and tune SQL, integrate data sources and BI tools, ensure data security and compliance, and collaborate with teams to meet business objectives.

Design, build, and maintain scalable data pipelines and ETL/ELT frameworks using Python and PySpark. Develop reusable components, microservices, and cloud-based data solutions on AWS and Snowflake. Ensure code quality via reviews, CI/CD, logging, and best practices while collaborating with stakeholders to solve data problems.

Design, build, and maintain scalable data pipelines and models using Databricks, dbt, and PostgreSQL. Develop AWS-based data solutions (Redshift, RDS, S3, Glue, Lambda, DMS), implement ETL/transforms in Python and Java, use Terraform for IaC, monitor with CloudWatch and Splunk, perform code reviews, and collaborate with stakeholders in agile teams to deliver reliable data systems.

Design, build, and operate large-scale data platforms and Spark/PySpark pipelines. Enable data integration, modeling, quality, and observability. Build MCP servers and AI-augmented tooling, mentor engineers, and lead cross-functional projects to deliver reliable data products.

Own and architect large-scale data pipelines, orchestration, and integrations. Drive IaC strategy (Terraform), third-party API and AI/LLM tool integrations, build export pipelines and internal tooling, mentor engineers, and align cross-team technical strategy for reliable, scalable data infrastructure.

Lead design and delivery of scalable data lakes, warehouses, and pipelines. Define governance, quality, monitoring, and CI/CD. Build real-time streaming systems and production AI/ML data flows (embeddings, vector stores, RAG, feature stores). Mentor engineers, set platform strategy, and drive data platform modernization.

Build and operate data-intensive web products and APIs, extend high-throughput ETL pipelines and scrapers, ensure reliability/observability, design self-scaling services, participate in code review, and collaborate across product, data, and infrastructure teams to meet production SLAs.

Develop and maintain Python-based data applications and pipelines: fetch data from databases/APIs/files, build Pandas DataFrames/NumPy arrays, write complex SQL and data transformations, handle nested JSON, and collaborate with data science teams to deliver production data platforms.

As a Lead Data Engineer, you will collaborate with Agile teams, develop technical solutions, and enhance cloud-based data warehousing to empower financial services for customers.

Build and maintain large-scale data processing pipelines using Apache Spark (PySpark/Scala) and Python. Design ETL workflows, implement distributed data processing, use version control (Git), and work with cloud platforms (GCP/AWS/Azure).

As a Data Engineer, you will build and optimize data pipelines, ensuring accurate data delivery that informs pricing and market analytics at Alt.

Build and maintain ETL/ELT pipelines, ingest and transform structured and unstructured data, integrate APIs and external sources, improve data quality and monitoring, support dashboards and ML pipelines, optimize warehouse and cloud performance, and contribute to data governance and documentation.

The Senior Bioinformatics Data Engineer will build data pipelines, implement clinical data ingestion, and optimize platform infrastructure while ensuring data quality and compliance with legal standards.

The Data Engineer will build and maintain scalable data pipelines, improve ETL processes, collaborate with AI/ML teams, and contribute to engineering culture.