Top Data Engineer Jobs

Design, build, and maintain data pipelines, ETL processes, and data warehousing solutions using Python and GCP/BigQuery. Ensure data quality, optimize storage and processing, support new data products, and mentor junior engineers while collaborating with the CTO and team.

Design, develop, and maintain scalable data pipelines, ETL processes, and BigQuery/GCP-based data storage. Ensure data quality, implement monitoring, assist product launches, mentor junior engineers, and collaborate closely with the CTO and team.

As a Data Engineer, you'll deliver scalable data solutions, participate in technical discussions, and ensure timely, trustworthy data delivery through robust pipelines.

Provide senior-level data engineering for analytics: resolve complex technical issues, build decision-support reports and dashboards, lead projects, prepare and integrate data, coach staff, and ensure high-quality analytics to support academic, research, and healthcare goals.

Design, build, and maintain scalable cloud data pipelines and Databricks-based data platforms. Manage Databricks clusters and Delta Lake tables, integrate with Azure services, implement CI/CD and monitoring, define data warehouse architecture and governance, collaborate with stakeholders, and support advanced analytics and future AI/ML use cases.

As a Data Engineer, you will design and implement data pipelines, analyze requirements, and develop software solutions to drive operational efficiency.

The Data Engineer will transform raw data into useful systems by building pipelines, conducting analysis, and collaborating on projects. Responsibilities include developing algorithms and enhancing data quality.

Design, build, operationalize, and maintain enterprise-scale data pipelines and platforms. Develop ETL/ELT workflows, parse structured and unstructured data, troubleshoot Palantir Foundry pipelines, and lead cross-functional teams to support analytics, monitoring, and scalable data solutions for mission-driven projects.

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.

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.

Designs and implements scalable cloud-based data pipelines and architectures, defines technical scope with stakeholders, monitors production reliability, mentors junior engineers, collaborates with data scientists and analysts, and creates reusable best practices and assets to deliver trusted data for decision-making.

Design, build, and maintain scalable data pipelines and a customer-facing recommendation engine (including LLM logic). Manage data warehouse, data quality monitoring, CI/CD for pipelines, and partner with product, analytics, engineering, and marketing to ensure consistent metrics and observability.

Build and maintain production-grade ETL/ELT pipelines for large supply-chain datasets (wafers, memory, assemblies). Ensure data quality, curate datasets, design optimized schemas, and enable downstream ML/AI models. Collaborate with procurement, operations, engineering, and IT to centralize legacy workflows and feed unified forecasting systems.

Design, build, and maintain scalable cloud data pipelines and platform components to power analytics, ML, and applications. Implement transformations, data quality checks, lineage and governance, optimize performance and cost, monitor platform health, and build reusable components across business units while collaborating with product and architecture teams.

Design, build, and maintain Azure-based data pipelines and architecture, ensure data quality and governance, document processes, monitor performance, and collaborate with analysts and data scientists to deliver reliable, traceable data assets.

Design and build large-scale ETL/ELT pipelines and analytical architectures on GCP. Assess, plan, and drive enterprise data and cloud migration programs, modernize legacy systems, optimize query performance, and enable scalable cloud-native solutions for clients.

Design, build, and maintain large-scale data pipelines and foundational datasets for recommendation systems. Source, validate, and improve data quality, implement monitoring and testing, and collaborate with ML and product teams while supporting production pipelines and on-call rotation.

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

Build and maintain data pipelines and ETL processes using Python and Databricks, manage relational databases, support ML/AI deployments, collaborate with engineers and data scientists, and ensure data availability and scalability for DoD/IC customers.

Design, build, and maintain scalable data pipelines and platforms (ETL/ingestion, batch and streaming) using Databricks and modern tooling. Collaborate with analysts, data scientists, and engineers to integrate disparate data sources, automate processes, and support analytics deployments. Produce reusable data solutions, follow source control best practices, and meet security/clearance requirements.

Lead design and implementation of ETL processes and scalable data pipelines. Build and maintain data infrastructure, enforce governance, monitor performance, collaborate with stakeholders, and mentor other data engineers while staying current with industry technologies.

Lead design and development of data engineering applications and teams, implementing ETL/ELT on data warehouse and data lake platforms (Ab Initio, Hadoop, Spark). Build batch and real-time data pipelines (Kafka), partner with product and architecture, drive DevOps deployments, mentor engineers, ensure scalability/resiliency, and support production operations and cloud migrations (AWS S3/Redshift).

Build and maintain scalable data pipelines, implement ingestion/transformation and data quality, design data models and export solutions, support production jobs, write tests, evaluate technologies, and collaborate with stakeholders to deliver analytics-ready data.

Build, operate, and optimize data platforms and pipelines (Snowflake, Azure ESB). Manage ETL, data migrations, ingestion, quality, and analytics tools (Cognos, Tableau). Collaborate with IT and business stakeholders, enforce data governance, support regulatory and cybersecurity requirements, and provide advanced support and system integration for banking APIs.

Build and maintain scalable data pipelines and enterprise DataMarts, apply advanced SQL to transform disparate sources into well-modeled, high-quality datasets, ensure data governance and integrity, optimize performance, and partner with analysts and stakeholders to enable reliable reporting and analytics.