Top Data Engineer Jobs

Design and maintain scalable batch and real-time data pipelines, build governed data models for a CDP, ensure data quality and security, support identity resolution and customer 360, mentor junior engineers, and collaborate with analytics and business stakeholders to deliver actionable customer insights.

Build, maintain, and support scalable data systems and pipelines. Ingest, clean, structure, and enrich data from multiple sources. Ensure data quality, security, and governance. Collaborate with analysts and data scientists to design solutions, deploy and optimize pipelines, and diagnose and resolve data issues to support analytics and reporting.

Responsible for building and operating scalable lakehouse data pipelines, optimizing data flows, and delivering analytics-ready data products using Azure Databricks technologies.

Design, build, and maintain scalable data pipelines and ETL processes in Azure (Synapse, Data Factory). Integrate data via APIs, ensure data quality/security, support analytics and Power BI reporting, collaborate with stakeholders, and manage work using Agile/Jira to support federal healthcare and VA initiatives.

The role involves engineering modern data platforms, focusing on data ingestion, transformation, and storage while utilizing Azure services and MS Fabric, supporting an investment management firm.

Design, build, and maintain scalable data assets and pipelines for analytics and ML; validate and ingest diverse data sources; perform exploratory data analysis and data quality checks; collaborate with cross-functional teams and produce reproducible code and documentation.

Design, implement, and maintain scalable data pipelines and storage for intelligence operations. Build ETL and real-time ingestion, optimize queries and data models, enforce data quality, governance, and security per IC standards, and collaborate with analysts and mission partners.

The Data Engineer will lead data lifecycle strategy, manage data acquisition and cleansing, and collaborate with teams to optimize data solutions and architecture.

Design, build, and maintain Microsoft Fabric-based enterprise data warehouse, ETL/ELT pipelines, and integrations. Ensure data quality, governance, and performant BI datasets while partnering with stakeholders and BI/Power BI teams. Provide technical leadership, troubleshoot pipelines, and recommend platform improvements.

Design, build, monitor, and troubleshoot ETL pipelines (Azure Data Factory) to move and transform large datasets from sources like Salesforce and SFTP into Snowflake. Implement SQL-based data cleansing and quality checks, document data lineage and mapping, collaborate with QA/UAT, and recommend new integration tools and efficiencies.

Lead design and delivery of scalable data warehouses and analytics platforms for T-Mobile Advertising Solutions. Architect and implement cloud data pipelines, mentor engineers, run design workshops, scope projects, and deliver end-to-end data solutions using cloud data services and modern data platforms.

Build and maintain data pipelines, applications, workflows, and governance in Palantir Foundry. Apply industrial engineering and operations research methods to analyze depot/MRO capacity, throughput, and bottlenecks, integrate ERP/MRO data into simulation models, deliver decision-support briefings, and support platform monitoring, automations, and client engagement.

Design, build, and maintain scalable data pipelines and platforms (ETL/ELT) using Databricks and cloud services. Collaborate with analysts, data scientists, and engineers to integrate disparate data, automate workflows, and deploy analytics workloads in agile teams to support mission-driven projects.

Develop and maintain Databricks Lakehouse ingestion and transformation pipelines (Bronze/Silver/Gold). Write SQL and PySpark, monitor jobs, document logic, participate in reviews, and collaborate across teams while learning team standards and cloud/data tooling.

Design, build, and maintain a Microsoft Fabric Lakehouse and Azure Synapse data warehouse. Develop and optimize ETL/ELT pipelines, data models, and Spark/SQL workloads. Implement data quality, monitoring, and runbooks; support production incidents and API integrations. Partner with stakeholders, follow Agile practices, and conduct code reviews to deliver reliable, secure analytics data for BI and advanced analytics.

Build and maintain greenfield data infrastructure and pipelines (ingest, validate, organize) for basketball and performance datasets. Implement medallion architectures, data validation, and optimizations; collaborate with cross-functional stakeholders to deliver analytic datasets for web and mobile applications.

Design and implement technical solutions with cross-functional teams, produce specifications and documentation, write/test/deploy code, perform and participate in code reviews, support and resolve technical issues, collaborate with QA, and stay current with emerging technologies to incorporate into solutions.

Design and build production-scale data pipelines, orchestration, and integrations on GCP to enable metadata-driven governance, lineage, automation, and AI-enabled workflows. Implement secure, observable, cost-efficient cloud data solutions, embed governance into engineering workflows, and drive CI/CD, testing, monitoring, and platform engineering best practices.

Design, develop, deploy, and support scalable data pipelines and data products. Collaborate with business and technology partners to shape solution architecture, optimize performance and cost, and promote reusable patterns. Support analytics, BI, and data science use cases while staying current on emerging data engineering and AI-assisted development technologies.

Design, develop, operationalize, and maintain enterprise-scale data applications and relational databases. Build scalable ETL/ELT pipelines, parse structured and unstructured data, and support data platforms on public cloud. Train, test, deploy, and maintain ML models, collaborate with interdisciplinary teams, and support mission-focused analytics for military and intelligence customers.

Design, build, and maintain scalable ETL/ELT pipelines (batch and streaming); manage data catalogs and API integrations with OAuth2; collaborate with cross-functional teams to ensure data quality and platform reliability for mission-driven projects.

Design and build scalable backend services and data ingestion pipelines, perform data modeling and large-file processing, develop reusable APIs, and optimize systems for speed and scalability while collaborating in an Agile team.

Build, maintain, and optimize reproducible data pipelines and large-scale relational databases for health disparities research. Collaborate with scientists to prepare, clean, transform, and integrate multi-source data, optimize pipeline performance, and produce datasets and visualizations for analysis and consortium distribution.

Lead design and implementation of a next-generation lakehouse data platform and high-scale batch/near-real-time pipelines. Own platform architecture, data modeling, governance, observability, and delivery to support BI, experimentation, and ML. Provide hands-on technical leadership, mentor engineers, and drive modernization toward an AI-first data engineering practice.

Design, implement, and deliver scalable data pipelines and integrations for government customers. Work with structured and unstructured data, APIs, databases, and cloud infrastructure (AWS). Collaborate with engineering, product, and data science teams to customize TRSS solutions, automate workflows, and provide solution architecture and operational support on-site in the Washington, DC area.