Top Hybrid Data Engineering Jobs

Seeking a Senior Data Engineer to design and optimize data pipelines, develop backend services, and implement Snowflake models, ensuring efficient data delivery in complex environments.

The Senior Data Modeler will design, develop, and optimize data models for financial systems, collaborating with teams to meet business requirements for analytics and decision-making.

The Lead Data Migration Specialist enhances system automation and accuracy, handling data collection, analysis, and onboarding. They resolve discrepancies, implement optimizations, and collaborate with development teams, ensuring efficient data management and continuous service improvement.

The Manager of Ontology and Data Modeling will develop and maintain enterprise ontologies, collaborate with teams, advocate for semantic technology, and support data integration efforts at Capital One.

The Data Engineer will build real-time data pipelines for pricing algorithms, collaborate with teams, and contribute to batch data workflows.

The Senior Data Engineer will design and implement scalable data models in BigQuery using dbt for analytics and reporting, ensuring data governance and optimal performance.

The Analytics Engineer will build data infrastructures, create dashboards, define metrics, structure datasets for AI projects, and foster insights across company functions.

The Data Engineer will build datasets and pipelines for marketing and sales reporting, collaborating with various teams to support data needs and ensure scalable solutions.

This role involves designing and implementing technical solutions for enterprise systems like SAP, translating business needs into functional designs, and ensuring system performance.

The role involves being a subject matter expert in Log Management, executing competitive strategies against legacy tools, architecting log-ingestion frameworks, and advocating for log consumption optimization with customers.

The Staff Data Engineer will architect, build, and maintain scalable data pipelines, implement ETL processes, and mentor team members.

Assist in developing and maintaining data processing pipelines, collaborating with teams to define requirements, and enhancing infrastructure for data sets.