Top Hybrid Data Engineering Jobs

The Data Migration Engineer migrates customer data into SaaS products, ensuring data quality and business continuity while collaborating with teams and managing customer requirements.

As a Senior Associate, you will assist clients in cloud data strategies, leveraging analytics, and aligning data architecture with business needs within a professional services framework.

The Sr. Business Intelligence Data Engineer designs data pipelines, optimizes ETL processes, and ensures data integrity in a Databricks environment while collaborating with cross-functional teams.

The Cloud Data Architect will design scalable data architectures for SaaS solutions, set data standards, collaborate with engineering, and ensure data governance and optimization. Requires advanced SQL and cloud platform proficiency.

The Analytics Engineer will enhance data assets, develop data models, optimize BI infrastructure, manage cross-functional partnerships, and ensure data quality through anomaly detection and documentation.

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.

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.

Work with internal and client teams to design and implement data-driven technology solutions. Apply SQL, Python, and data platform knowledge to process structured/unstructured data, build BI and engineering artifacts, communicate findings to stakeholders, and adapt to new tools and architectures while supporting client-facing delivery.

The Sr. Analytics Engineer at Philo is responsible for designing data models, governing business metrics, collaborating with stakeholders, and ensuring data quality and documentation for analytics purposes.

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.