Top Data Engineer Jobs

As a Data Engineer, you'll develop scalable data pipelines, build applications, integrate data sources, and support decision-making efforts while collaborating with team members.

Lead design and deliver cloud-based data engineering solutions across Agile teams. Build, test, and support full-stack data systems using big data, streaming, and cloud technologies; mentor engineers and ensure high-performance, well-tested code.

Lead projects as a Data Engineer, implementing data solutions, mentoring staff, optimizing data pipelines, and ensuring data quality using Azure technologies.

The Associate Data Engineer will build and optimize data infrastructure, transform raw data into insights, develop data pipelines, and ensure data quality and governance.

Design and build data pipelines using AWS services and Python. Collaborate with data teams to ensure data quality and optimize performance.

As the first Data Engineer, design and implement a scalable data platform, build data pipelines, and establish data modeling standards. Enable BI accessibility and document core metrics.

As a Data Engineer, design and maintain data pipelines, ensure data quality, and support AI workflows using modern tools and cloud environments.

The job involves leveraging AWS and Snowflake for data engineering tasks within a technology-focused company, requiring US residency.

Lead the development of AI data pipelines, improve data capabilities, integrate AI solutions, mentor team members, and ensure operational efficiency.

The AWS Cloud Data Engineer will design, develop, and enhance AWS data environments, build scalable Data Lake solutions, and lead proofs-of-concepts into production.

Design, build, and maintain scalable GCP data pipelines and ETL/reverse ETL processes using Dataflow, Dataform, and BigQuery. Develop secure APIs for ingestion, optimize data models and Datastream integrations, implement monitoring, and ensure GDPR/CCPA-compliant data governance for player analytics and fraud detection.

Design, build, and maintain scalable data pipelines, automate workflows with Airflow/Python, develop PostgreSQL models, deploy analytics across Linux environments, and create Power BI datasets and dashboards to support Finance, Operations, and Customer tools.

Build and own end-to-end sales data pipelines and curated marts in Snowflake/dbt; implement monitoring, tests, and governance; run reverse-ETL to operational systems; partner with Sales, Ops, Finance, and People to deliver production-ready analytics products.

Lead design, build, and maintain scalable data pipelines and warehouses. Drive technical roadmap, mentor engineers, implement data quality/testing/monitoring, optimize performance and cost, and collaborate cross-functionally to ensure reliable data for BI, ML, and applications.

As an AI Data Engineer, ensure the reliability and accuracy of AI Agents through systematic evaluation and validation, utilizing strong analytical skills and data quality principles.

The Staff Engineer - Finance Data Specialist will develop and support a Finance Data Warehouse, collaborating with Finance and Tech teams on data management and analytical solutions.

The Sr. Data Engineer is responsible for application development, database performance monitoring, data analytics, and ensuring compliance with quality standards.

The HR Data Engineer designs and maintains data pipelines, transforms HR data into insights, and ensures data accuracy, governance, and security while supporting analytics initiatives.

Design, develop, and manage data infrastructure for internal and client projects, focusing on large-scale data pipelines, APIs, and cloud deployments.

The Lead Data Consultant will design and deliver scalable data solutions, manage project teams, and optimize client data environments, ensuring effective project execution and communication.

Lead design and build of a scalable, secure cross-cloud data platform and marts. Drive selection and optimization of the modern data stack, enforce governance and lineage, and establish reusable data models and best practices to support analytics, BI, and ML use cases across product and engineering teams.

Lead design and implementation of a unified event-tracking data platform. Collaborate with Data, Product, and Engineering to build reusable event-publishing frameworks, monitoring/alerting, and deprecate legacy systems. Coach mid-level engineers, influence roadmap and processes, manage cloud/SaaS spend, create documentation, and participate in on-call rotations to ensure platform reliability and data trustworthiness.

The role involves developing scalable data pipelines, automating workflows, optimizing data models, and ensuring data governance while collaborating with various teams.

Join a 3-month immersive program to work on automation and data engineering projects, with mentorship and potential for a full-time role.

The Data Engineer will build and maintain scalable data pipelines, optimize data for AI/ML models, and collaborate with engineers to ensure data quality and integration across systems.