Top Hybrid Data Engineering Jobs

As a Data Engineer II, you will build and optimize data pipelines in Databricks, ensure high-quality data delivery, and support generative AI applications. You will collaborate with data scientists and AI engineers to provide reliable data infrastructures and meet business needs.

Design, build, and maintain scalable data pipelines and API integrations; implement data quality monitoring and alerting; troubleshoot data issues; collaborate with business stakeholders; support CI/CD processes and scale architecture for future needs.

As a Data Migration Engineer, you'll convert client banking data to the Q2 platform, troubleshoot issues, and manage multiple migration projects.

The SOX Data Analytics & AI Manager will develop and implement data analytics and automation solutions for SOX compliance, lead a team, and collaborate across functions to enhance efficiency in internal controls.

The Finance Systems Architect is responsible for designing and optimizing finance technology solutions, ensuring alignment with business needs, and leading initiatives for system integrations and process automation.

Lead the Data Science team to innovate credit forecasting models, leveraging machine learning and data infrastructure to improve customer products and experiences.

Manage the data architecture and strategic planning for the Sales team, optimizing processes through data and AI for targeting accounts and pricing strategies.

Lead the architecture and design of data and analytics solutions, focusing on ETL, data warehousing, and cloud technologies while collaborating with global teams to optimize data processes.

As a Data Engineer, you'll build and operate cloud data platforms, develop ETL/ELT pipelines, and ensure data quality for analytics and reporting.

The Data Engineer will lead data projects, collaborate with stakeholders, develop data transformations, and maintain data quality for business needs.

Seek a Lead Analytics Engineer to design and build scalable data solutions, oversee data engineering, and collaborate with cross-functional teams to meet data needs.