Top Data Engineer Jobs

As a Data Engineer, you will develop and maintain data pipelines for real-time insights, optimize data quality, and collaborate across teams to deliver accurate data products.

As a Data Developer, you will create reliable ETL processes, design data warehouses, and develop reports while ensuring data quality and security.

Lead the development of data engineering processes, ensuring data quality and security while working with data science and business intelligence teams. Responsible for building scalable data pipelines and maintaining them.

The Data Engineer II designs, develops, and maintains data integration and analytics solutions, ensuring data quality and accessibility for business stakeholders, while collaborating with teams to meet business needs.

The Data Engineer will design, develop, and implement data solutions, ensuring data integrity and availability, collaborating with stakeholders to meet business analytics needs.

As a Senior Data Engineer, you will enhance data products, build automated data pipelines, and improve data platform capabilities, collaborating with various teams to drive initiatives.

The Junior Data Engineer will work with data to enhance trading systems, support data warehouses, and collaborate on technical solutions with the team.

Design, develop, and deliver data solutions and database systems, maintain data pipelines, automate processes, and support internal teams with data infrastructures.

The Technology Specialist - Data Engineer will focus on data integration, pipeline creation, data governance, and providing insights using BI tools, particularly Power BI. They will ensure optimal data quality, performance tuning, and automation in Azure environments, while also contributing to infrastructure management and DevOps practices.

The Data Engineer will manage data extraction pipelines, modernize data systems, implement monitoring tools, optimize workflows, and support compliance requirements using various tools and platforms.

As a Data Engineer at Waymo, you'll design and implement data models and data warehousing solutions, ensuring data quality and collaboration with stakeholders.

The Data Engineer will manage and develop data warehousing solutions, implement ETL processes, and utilize SQL for data analysis and reporting. Experience in analytics tools and communication is essential.

The Data Engineer will gather and process large datasets, develop algorithms, conduct data analysis, and improve predictive models for business decisions.

As a Data Engineer, you'll create and maintain data pipelines, optimize data architecture, and support analytics tools for performance insights.

The Analytics Engineer transforms raw data into quality data products for BI and reporting, manages data modeling, and ensures data accuracy through collaboration and automation.

Design, build, and maintain scalable cloud-native ETL/ELT pipelines and serverless workflows using Python, Airflow, and AWS. Implement dbt transformations, manage DynamoDB and other storage, enforce data quality and governance, automate CI/CD, and mentor junior engineers while collaborating with analytics, product, and creative teams.

Design, develop, and manage Control-M job flows and enterprise data pipelines. Build job dependencies, monitoring, automation, reruns, and SLA management. Integrate with data platforms (warehouses, Hadoop/Spark), cloud services (AWS/Azure/GCP), SFTP/APIs/storage, and implement scripting (shell/Python) and CI/CD for reliable, high-performance data ingestion, transformation, validation, and downstream delivery.

Design and maintain scalable, secure data pipelines and contracts for cloud and on-prem environments. Implement real-time processing and observability tooling, collaborate with product teams, and support asset visibility workflows for OT/ICS customers.

Lead design, build, and support of cloud-based ETL, data warehousing, and reporting solutions. Develop/optimize ETL pipelines, data models, and queries; deploy containerized services on Kubernetes; automate workflows; mentor teammates; collaborate with architects, data scientists, and stakeholders to deliver scalable analytics and reporting products.

Provide technical leadership to design and implement application integrations and end-to-end data solutions. Develop and convert SSIS/SSRS/SSAS and SQL Server packages, build data pipelines, ensure data quality and lineage, support cloud migration to AWS S3/EMR and Snowflake, produce artifacts in GitHub, and mentor junior developers throughout the SDLC.

The Data Engineer will design, maintain, and scale ETL pipelines for blockchain data, optimize data models, and ensure timely delivery of features in alignment with product goals.

Lead ingestion and configuration of clinical trial data using tools like CDB/Illuminate. Manage data imports/exports, JSON manifests, and data mappings. Support data configuration workflows, work with SDTM structures, and collaborate with Data Engineering and stakeholders to deliver independent projects.

Design, optimize, and scale large data pipelines and lakehouse architectures across Hadoop and cloud. Lead technical design, improve data quality and observability, mentor engineers, and drive platform modernization (cloud migrations, Databricks, Delta Lake, CI/CD).

The Senior Data Engineer will manage end-to-end data processes, develop ETL/ELT pipelines, and provide analytics-ready outputs using Python, Azure, and Snowflake.

The Principal Data Analytics Engineer will design complex data models, optimize querying processes, mentor team members, and ensure data quality across financial datasets.