Top Remote Senior Level Data Engineering Jobs in Boston, MA

Designs and develops scalable, reusable data solutions, production pipelines, reporting dashboards, and proof-of-concepts for business stakeholders. The role includes data engineering, infrastructure optimization, data quality testing, metadata and lineage management, compliance, security, and Gen AI applications. Responsibilities also include evaluating tools, mentoring junior team members, prioritizing customer-focused solutions, and collaborating with technical and non-technical stakeholders.

Designs and develops scalable, reusable data solutions, production pipelines, dashboards, and reporting tools. Builds proof-of-concepts, optimizes data infrastructure, implements data quality testing, and applies metadata, lineage, compliance, and security principles. The role also evaluates data engineering technologies, explores generative AI applications, supports stakeholder needs, prioritizes customer value, and mentors junior team members.

Designs and develops scalable, reusable data solutions, production pipelines, reporting dashboards, and proof-of-concept systems. Responsibilities include data engineering, infrastructure optimization, data quality testing, metadata and lineage management, compliance, security, GenAI applications, and iterative solution testing. The role collaborates with technical and non-technical stakeholders, prioritizes customer value, mentors junior team members, and evaluates emerging data engineering tools and technologies.

Lead RevOps data modeling, ETL/DBT development, and dashboarding to ensure high-quality revenue data. Build Snowflake data marts, author complex SQL, create Tableau visualizations, apply AI/ML forecasting, and partner with Sales, Marketing, Finance, and IT to resolve data quality and reporting issues.

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.

The Senior Analytics Engineer will design and implement scalable data solutions for Finance Analytics, reconcile data accuracy, and collaborate across teams to enhance financial datasets and reporting workflows.

Lead the architecture and migration of Rula’s data platform to Snowflake and Medallion architectures. Govern warehouse performance and cloud costs, establish data contracts and intake guardrails, and enforce GitHub-based CI/CD testing for data quality and query performance. Scale AI-assisted engineering practices across teams while partnering with executives and domain engineers. The role also supports secure clinical data handling, schema evolution, semantic BI integrations, and production ELT pipeline development.

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.

Leads and develops a data engineering team supporting People Analytics and AI-assisted workforce analytics. Designs scalable data models, pipelines, semantic layers, testing frameworks, and governance practices. Remains hands-on with production code, reviews, and technical problem-solving while partnering with People Analytics, business, legal, and compliance stakeholders. Establishes engineering standards across Python, SQL, dbt, Airflow, Snowflake, CI/CD, data quality, observability, privacy, and security.

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.

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.

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.