Senior Databricks Data Engineer #Senior Databricks Data Engineer

Be Part Of A High-Performing Global Financial Technology Team:

This opportunity sits within the technology division of a globally recognized financial institution known for its strong presence in capital markets, risk management, and enterprise banking solutions. The organization operates at scale, supporting mission-critical systems across multiple regions with a strong emphasis on stability, performance, and regulatory alignment. The technology teams are highly collaborative, operating in a fast-paced, delivery-focused environment where engineering excellence and data integrity are paramount. This role supports risk technology initiatives that enable advanced analytics and enterprise data capabilities across the organization.

What’s In Store For You:

This is a hybrid opportunity based in Jersey City, NJ, offering the chance to work on enterprise-grade data platforms within a mature, structured environment. You will collaborate with experienced engineers, data architects, and risk stakeholders on high-impact initiatives. The role offers exposure to large-scale cloud data ecosystems, modern data engineering practices, and long-term platform evolution within financial services.

How You Will Make An Impact:

• Design, build, and maintain scalable ETL/ELT pipelines using Databricks and Python.
• Develop and optimize large-scale data processing workflows supporting risk and analytics use cases.
• Ensure high standards of data quality, validation, and governance across ingestion and transformation layers.
• Architect and optimize cloud-based data storage solutions for performance and scalability.
• Collaborate with cross-functional teams including data consumers, architects, and database specialists to deliver reliable data assets.
• Troubleshoot and resolve complex data performance and integration issues.

Are you a proven Databricks data engineering professional ready to make an impact?

• 10+ years of experience in data engineering, data warehousing, or large-scale data platform development.
• Strong hands-on expertise with Databricks (must-have).
• Advanced proficiency in Python and SQL for data transformation and optimization.
• Experience with cloud-based data tools such as AWS Glue, Azure Data Factory, or equivalent.
• Deep database expertise, including performance tuning and optimization.
• Experience building enterprise-grade ETL pipelines in hybrid or cloud environments.
• Strong understanding of data quality frameworks and best practices.
• Ability to work effectively in hybrid environments and collaborate with distributed teams.
• Experience within financial services or regulated industries preferred.