Top Data Engineer Jobs

Build scalable data foundations, analytics tooling, analysis pipelines, and trusted self-service datasets for product and data science teams. The role focuses on instrumentation, experimentation standards, data quality, metric governance, documentation, workflow orchestration, and cross-functional collaboration. Engineers will use SQL, Python or R, and AI-assisted workflows while validating results and protecting sensitive data.

Designs, builds, and operates production data pipelines on Databricks and Azure using Python, SQL, Spark, Delta Lake, dbt, and Dagster. Responsibilities include ingestion, medallion-layer transformations, dimensional modeling, ML datasets, data contracts, quality testing, governance, CI/CD, observability, performance tuning, troubleshooting, and on-call support. The role partners with architects, analysts, product owners, and business stakeholders to deliver reliable, reusable data products.

Build and maintain modern data-centric applications, pipelines, transformations, enrichment processes, provisioning layers, and user interfaces supporting clinical and operational healthcare processes. Analyze clinical data and report trends using cloud, big data, data science, and modern software development practices. Collaborate with Product, Platform, and Architecture teams, mentor Data Engineers, document solutions, manage source control, and research emerging technologies.

Builds and optimizes enterprise quality data solutions, including pipelines, integrations, databases, dashboards, analytics platforms, and cloud technologies. Partners with Quality, IT, business teams, and vendors to translate requirements into scalable data, automation, reporting, and AI solutions. Governs data management, security, compliance, and lifecycle practices; supports quality systems and users; analyzes quality trends; and drives continuous process improvement. The role also leads AI-enabled solutions using Microsoft Copilot Studio, AI Builder, and Power Platform.

Build and operate reliable data infrastructure, including event-driven architectures, streaming and ingestion pipelines, API integrations, and automated workflows. Develop agentic systems that interpret events and trigger business actions, while ensuring data quality, observability, error handling, and safe execution. Partner with analytics teams, contribute to data architecture strategy, and manage multiple engineering projects. The role requires strong Python expertise and experience with real-time data systems, orchestration, and integrations.

Build and operate governed production data pipelines in Databricks using medallion architecture, Unity Catalog, data quality controls, lineage, and semantic models. Develop AI agents and retrieval-augmented applications grounded in enterprise data using Mosaic AI and model serving. Integrate enterprise systems through APIs and events, implement security and observability, and own architecture, testing, CI/CD, deployment, and production support.

Designs, develops, and optimizes scalable data models, cloud-native platforms, and batch and real-time pipelines for an AI/ML disability claims program. Responsibilities include data quality, governance, security, database optimization, ETL automation, observability, ML pipeline enablement, and cloud migration. The role mentors engineers, leads cross-functional initiatives, documents architecture, and ensures reliable, high-performance data systems supporting analytics and machine learning.

Leads the architecture, development, and operation of scalable cloud data platforms and complex ETL/ELT pipelines. Partners with senior stakeholders to define data strategy, architectures, and technical roadmaps. Mentors data engineers, establishes engineering and governance standards, and owns production reliability, data quality, observability, and incident response. Evaluates generative AI and agentic systems for data engineering while promoting responsible AI practices. Requires advanced Python, SQL, cloud, distributed processing, integration, DevOps, and data architecture expertise.

Designs and maintains scalable ETL pipelines for structured and unstructured data, supporting DEA legacy-data migration, validation, integration, and modernization. The role improves data quality, interoperability, reliability, and cloud-platform performance while preparing data for analytics and AI/ML use cases. Responsibilities include source-to-target mapping, troubleshooting, documentation, stakeholder collaboration, and participation in Agile development.

Develops and maintains HCM dashboards, APIs, data pipelines, and analytical products for USDA modernization initiatives. Translates business requirements into secure, scalable data solutions; supports migration planning, data quality, governance, metadata, systems integration, testing, validation, and Databricks Lakehouse architecture. Responsibilities also include requirements analysis, UAT coordination, documentation, training, and lifecycle management of HCM data products.

Build, deploy, and maintain reliable data infrastructure, including ingestion and ETL/ELT pipelines, data quality processes, warehouses, automation, orchestration, and lightweight data applications. Optimize data stack performance, document pipelines, and collaborate with analysts, data scientists, and stakeholders. Curate datasets supporting operational models, feature stores, and AI/ML workflows while continuously learning data engineering best practices.

Build and operate scalable AWS data platforms, batch and near-real-time pipelines, ETL/ELT workflows, and governed data lake or lakehouse architecture. Lead data integration for organizational acquisitions, improve data quality and observability, and support Power BI reporting through curated datasets and semantic models. Partner with technical and business stakeholders across editorial, subscriptions, advertising, finance, marketing, and operations to deliver reliable analytics and reporting foundations.

Architect, build, and operate low-latency market data systems, including feed handlers, normalization, distribution, and exchange connectivity. Design high-throughput infrastructure for real-time trading data, improve reliability and performance, participate in observability, on-call, and incident response, and deliver well-tested features end to end. Partner with engineering, product, and cross-functional stakeholders while leading a lean, high-impact team.

Design and deliver modern data platforms, scalable and secure ingestion and transformation pipelines, durable data models, streaming systems, and reporting layers. Partner directly with clients and business stakeholders to translate requirements into architecture, explain tradeoffs, and own delivery outcomes. The role includes production implementation across cloud data platforms, orchestration, governance, real-time technologies, and analytics or AI data consumption.

Build and maintain governed Silver/Gold data models and semantic assets on Databricks. Translate stakeholder KPIs into testable, reusable transformations, apply enterprise modeling standards, enable domain self-service, ensure data sensitivity and governance, and review domain-contributed models for production readiness.

Build Sailor Health’s foundational data platform from scratch. Responsibilities include developing Snowflake and dbt pipelines integrating EHR, CRM, and communications data; creating data-powered web applications; automating clinical and billing workflows with Python; analyzing therapy transcripts and patient data using AI and machine learning; tracking clinical outcomes; and designing scalable data architectures in collaboration with clinicians, engineers, founders, and other teams.

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 and maintain automated data pipelines, ETL/ELT workflows, integrations, migration processes, and CI/CD automation for USDA’s HCM modernization initiative. Responsibilities include data mapping, transformation, cleansing, validation, reconciliation, testing, monitoring, and exception handling. Support Databricks Lakehouse architecture, security, common data models, reporting, analytics, and DevSecOps practices while coordinating mock migrations and UAT.

Leads the design and implementation of enterprise data platforms, pipelines, ingestion systems, and analytics products. Develops production-grade Python applications, manages AWS, Snowflake, and dbt environments, establishes engineering and governance standards, and improves observability, security, quality, and cost efficiency. Integrates disparate data sources and supports generative AI, RAG, and LLM applications while partnering with business, software, DevOps, and data science teams.

Designs, builds, and operates scalable batch and streaming data platforms using Spark, Python, SQL, Microsoft Fabric, and Lakehouse architectures. Develops ETL/ELT pipelines, dimensional models, data governance, observability, and CI/CD practices. Optimizes distributed workloads, supports AI and analytics use cases, leads technical design, and mentors engineers while partnering with architects, stakeholders, security, and governance teams.

Build and operate reliable data pipelines transforming flight test, simulation, and operational data into versioned, traceable ML training datasets. Responsibilities include ingestion, normalization, time alignment, validation, dataset lineage, anomaly mining, labeling workflows, deployment feedback loops, monitoring, and data coverage analysis. Collaborate with flight test and operations teams on instrumentation and data access. The role is on-site in Boston and requires production data engineering experience supporting machine learning pipelines.

Lead enterprise-scale data architecture and engineering for Xometry’s Data Platform, including scalable batch and streaming pipelines, partner integrations, data models, governance, observability, and low-latency event-driven flows. Own the data plane connecting Xometry with Teamcenter and Designcenter, establish engineering standards, develop technical roadmaps, collaborate across teams, and mentor engineers. The role requires strong SQL, Python, cloud data warehouse, AWS, modern data tooling, CI/CD, testing, and enterprise integration expertise.

Own enterprise-scale data architecture and engineering for Xometry’s data platform, including real-time partner integration pipelines connecting manufacturing systems. Design scalable batch and streaming systems, data models, governance, observability, testing, and CI/CD practices. Lead multi-quarter technical roadmaps, collaborate across engineering, product, analytics, machine learning, and external partners, and mentor engineers. Support the DFM AI and IQE integration across Teamcenter, Designcenter, and related systems.

Build, deploy, and operationalize complex data pipelines and solutions using modern data platforms and ETL tools. Design data architectures, integrate sources, improve data quality, and support AI, machine learning, and business intelligence initiatives. Analyze and resolve complex data defects and incidents, apply governance and security practices, and serve as a data engineering subject matter expert. Guide team members, educate business users, communicate technical concepts, and provide technical leadership across enterprise data projects.

Build and lead Cognite’s enterprise data warehouse, semantic layer, data pipelines, integrations, and business data models. Use Azure, Microsoft Fabric, Power BI, Python, SQL, APIs, and Cognite Data Fusion to deliver reporting and AI-ready data solutions. Establish data governance, security, privacy, and quality standards while partnering with Finance, Sales, Marketing, HR, Legal, Engineering, and other stakeholders.