Top Hybrid Data Engineering Jobs

The Sr. Specialist executes data integration and platform requirements in Marketing, supporting large-scale data initiatives and documentation processes alongside cross-functional teams.

The Senior Data Engineer designs and implements enterprise-scale data solutions, leading data migration initiatives and optimizing data pipelines using Azure technologies, while mentoring junior engineers and ensuring data quality.

The Senior Data Engineer will transform real-world data into analytics-ready datasets, build scalable pipelines, and conduct analyses to support scientific and business decisions, collaborating closely with data science teams.

The Senior Data Engineer will build and scale data infrastructure, create data models, develop ingestion pipelines, and ensure data quality for drug development at Formation Bio.

The role involves implementing mortgage prepayment models into a financial analytics library using C++, requiring collaboration across teams and adherence to software engineering practices.

Lead a team of data engineers and analysts, manage cross-functional data projects, and integrate insights to improve product and customer value.

The Senior Analytics Engineer will build and scale the analytics layer for decision-making. Responsibilities include design, implementation, testing, and maintaining data reliability across teams.

The Data Modeler will design and maintain data models, support data architecture, collaborate with product teams, and ensure quality and governance of data products.

The Senior Data Engineer will manage data flow and ensure accuracy, improve data engineering stacks, build and maintain analytics warehouses, and support stakeholders with data solutions while participating in system reliability on-call rotations.

As a Senior Data Engineer, you will design data pipelines, manage databases, optimize data workflows, ensure data integrity, and collaborate with cross-functional teams on data-driven solutions.

The Data Specialist analyzes technical performance using data analytics, ensures solutions conform to models, and resolves data inaccuracies while working on cloud computing solutions and implementing ETL processes.