Top Data Engineer Jobs

The Data Platform Engineer will develop and maintain scalable data platforms, implement data integration solutions, and mentor teams on best practices and standards.

The Sr Data Engineer will design and implement data solutions for Disney+, manage data quality, and work with teams to define requirements for new data features.

The Senior Data Engineer will build and manage data pipelines, optimize data structures, automate workflows, and collaborate with teams to ensure data reliability.

As a Senior Data Engineer, you will build and optimize foundational datasets, data pipelines, and analytical dashboards while ensuring data quality management.

Design, build, and maintain scalable data pipelines using Fivetran and Snowflake. Develop and optimize dbt models, ensure data quality and governance, implement data modeling best practices, troubleshoot performance, and collaborate with analysts and business teams to improve data accessibility and engineering practices.

The Data Integration Engineer will manage data integrations and ETL processes for healthcare data, ensuring accuracy and accessibility for business users.

As a Senior Data Engineer, you will design and optimize data pipelines, enhance system efficiency, and collaborate with teams to drive health outcomes using large healthcare datasets.

You will build and operate a validated data platform, ensuring data integrity, mentoring team members, and collaborating on data discoverability and queryability.

As a Senior Data Engineer, you will enhance the data lakehouse, optimize data workloads, perform complex transformations, and develop frameworks to improve delivery velocity, focusing on cybersecurity solutions.

The Senior Data Engineer will architect and enhance data infrastructure, create dependable data pipelines, oversee cloud-based data warehouses, and mentor engineers to enable scalable solutions for analytics and machine learning.

The Sr. Data Engineer will design and maintain data pipelines, ensuring data integrity across the organization and collaborating with data scientists and analysts.

Design and optimize data pipelines, automate ingestion frameworks, and enhance data layers within GCP for healthcare analytics and reporting.

The Lead Data Engineer will design and optimize scalable data pipelines within GCP for healthcare analytics and reporting, lead automation efforts, and mentor junior engineers.

The Data Analytics Platform Engineer will design and implement data pipelines, manage platforms, and provide data insights for various missions, focusing on cybersecurity and operational efficiency.

The Senior Data Engineer II will build and optimize data solutions on Axon's Corporate Data Platform, design data pipelines, mentor team members, and uphold data governance and quality standards.

Design, build, and optimize scalable data ingestion and transformation pipelines in a medallion (Bronze/Silver/Gold) architecture on Microsoft Fabric/OneLake. Ensure HIPAA-compliant, multi-tenant data isolation, data quality monitoring, lineage and governance. Partner with Data Science, ML Engineering, and Software Engineering to deliver feature stores, ML-ready datasets, and production-grade data delivery for analytics and scoring.

Design, execute, and support end-to-end SAP S/4HANA data migrations using LTMC, SLT, and LVM. Perform extraction, transformation, cleansing, validation, reconciliation, and cutover activities. Optimize large-volume loads, manage replication/configuration, support mock migrations and cutovers, and maintain technical documentation and runbooks while collaborating with functional teams and ensuring data governance and compliance.

Lead design, development, and optimization of cloud-native ETL/ELT pipelines and data lake architectures on AWS. Build scalable ingestion and streaming pipelines using Talend Cloud, PySpark, and Python microservices; leverage S3, Glue, EMR, Lambda, and Athena. Ensure data quality, governance, performance tuning, and operational monitoring; troubleshoot production issues, collaborate with stakeholders, and mentor junior engineers.

Design, build, test, and maintain high-quality data pipelines and storage systems. Collaborate with architects, analysts, scientists to ingest, transform, and prepare data for analytics and ML, implement DevOps best practices, write tests and documentation, and improve data reliability, performance, and security using modern cloud and big-data technologies.

Design, build, and scale reliable batch and real-time data pipelines and warehouses. Partner with engineering, data science, and business teams to define data models, improve observability, optimize storage and querying, and evangelize data engineering best practices across the company.

Design, build, and maintain scalable ELT/ETL data pipelines and data models for healthcare analytics. Ensure data quality, observability, performance optimization, and documentation. Collaborate with stakeholders and analysts, perform troubleshooting, conduct code reviews, mentor team members, and support data governance and accessibility efforts.

Lead architecture and implementation of scalable data systems and pipelines from ingestion to delivery. Own ambiguous, high-impact integrations, improve data quality and reliability, adopt AI-augmented engineering, mentor engineers, and collaborate with Product, ML, and customer teams to ship production solutions.

Lead end-to-end analytics solutions: gather requirements with stakeholders, build production-quality ETL and visualizations, coach junior analysts, communicate progress and roadblocks, support proposals and pitches, and contribute to firm initiatives and client delivery.

Design, build, and optimize Snowflake-based data platforms and DBT models. Develop ingestion/replication pipelines, profile source data, ensure data quality, and support analytics and reporting through advanced SQL and collaboration with architects and stakeholders.

Design, build, and maintain scalable batch and streaming data pipelines and analytics-ready data models. Implement cloud (primarily AWS) data platform solutions, data ingestion, transformation, orchestration, and observability. Embed data quality, governance, security, and metadata/lineage practices. Partner with architecture, analytics, and application teams to enable self-service analytics, reporting, and downstream use cases while driving platform evolution and engineering best practices (CI/CD, testing, documentation).