Top Hybrid Data Engineering Jobs

The Senior Staff Data Engineer will design scalable data systems, lead technical direction, optimize data platforms, and ensure data quality and performance for Observability & Insights.

Lead and grow a data engineering team that designs, builds, tests, and operates large-scale data pipelines and warehouses. Partner cross-functionally to translate requirements, ensure data quality, observability, SLAs, security, and governance, and drive automation and performance for analytics, ML, and product use cases.

The Senior Ad Ops Engineer develops and manages data products for marketing activation, focusing on data pipelines, quality validation, and integration with marketing platforms.

The Analytics Engineer will build and optimize data transformation models, assist with BI tools, support stakeholders, document processes, and implement AI tools, contributing to a data platform under mentorship.

The Director of Analytics Engineering will lead the strategy and execution of data warehousing and analytics for Current, managing a team and ensuring data integrity.

The Senior Snowflake Data Engineer designs and optimizes Snowflake architecture and pipelines, ensuring data governance and performance across Marketing and Sales teams.

As a Data Engineer, you will develop scalable data pipelines, optimize performance, ensure data reliability, and collaborate with cross-functional teams on AI-driven features and analytics.

The Senior Data Engineer will build and optimize data pipelines, ensure data integrity, and collaborate with stakeholders to meet business requirements while implementing best coding practices.

The Senior Data Platform Engineer will design, maintain, and enhance Hinge's data platform, including data pipelines, monitoring systems, and user feedback incorporation while ensuring data quality and system reliability.

The role involves leading data architecture strategies and driving innovation in data solutions while ensuring compliance with governance policies. Responsibilities include translating data requirements into technical plans, overseeing advanced data technologies, and fostering collaboration among stakeholders.