Top Hybrid Data Engineering Jobs

Build and own end-to-end sales data pipelines and curated marts in Snowflake/dbt; implement monitoring, tests, and governance; run reverse-ETL to operational systems; partner with Sales, Ops, Finance, and People to deliver production-ready analytics products.

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.

Lead design, build, and maintain scalable data pipelines and warehouses. Drive technical roadmap, mentor engineers, implement data quality/testing/monitoring, optimize performance and cost, and collaborate cross-functionally to ensure reliable data for BI, ML, and applications.

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.

Design, build, and operationalize cloud-based data pipelines and transform raw data into analysis-ready datasets. Collaborate with business, analytics, and engineering teams to develop data models, automate data preparation, and document transformations. Support Power BI models and dashboards, improve data accessibility and reliability, and mentor peers while exploring new data technologies and automation techniques.

Lead the evolution of engineering metrics. Prototype and develop high-complexity data solutions to enhance software development workflows, integrating AI impact assessments and influencing strategic decisions across the organization.

Lead design and integration of pricing applications across enterprise environments. Provide architecture guidance, oversee integrations, ensure security and compliance, manage and mentor architects, and align solutions with business and IT stakeholders to deliver scalable pricing and revenue management solutions.

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.

The Senior Lead Securities Quant focuses on developing asset liability management models, enhancing quantitative risk metrics, and implementing scalable analytics solutions in collaboration with various stakeholders.

Manager of Data Platform Engineering responsible for ensuring data reliability, leading a data engineering team, setting platform standards, and collaborating with internal stakeholders to enhance data capabilities.

Curate, cleanse, validate, and maintain large US equity and options datasets; integrate fundamental and alternative data; build data pipelines for research and trading; perform statistical and ML analysis to identify market patterns; document data provenance and collaborate with engineers and traders to deploy production-grade models.