EDUCATION DESIRED :

Bachelor of Computer Science

EEOC STATEMENT

FIS is an equal opportunity employer. We evaluate qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, marital status, genetic information, national origin, disability, veteran status, and other protected characteristics. The EEO is the Law poster is available here supplement document available here
For positions located in the US, the following conditions apply. If you are made a conditional offer of employment, you will be required to undergo a drug test. ADA Disclaimer: In developing this job description care was taken to include all competencies needed to successfully perform in this position. However, for Americans with Disabilities Act (ADA) purposes, the essential functions of the job may or may not have been described for purposes of ADA reasonable accommodation. All reasonable accommodation requests will be reviewed and evaluated on a case-by-case basis.

ABOUT THE ROLE

We are seeking a highly skilled Senior Data Engineering Manager to lead our data engineering team within the Enterprise Data and AI team. This role is pivotal in developing scalable data and analytics solutions, implementing high-quality codebases, and collaborating with various teams to drive product innovation. The ideal candidate will have extensive experience with Azure, Fivetran, Snowflake, Databricks, and a strong understanding of data architectures within the FinTech industry.

WHAT YOU WILL BE DOING

• Lead and manage a team of data engineers to design, develop, and maintain scalable data pipelines and platforms.
• Architect and implement robust data solutions using Fivetran, Azure Data Services Snowflake, and Databricks.
• Ensure data integrity, security, and governance across all data initiatives.
• Collaborate closely with business partners, managers, and engineers to facilitate needs into technical solutions and drive product innovation.
• Drive ETL/ELT best practices and optimize data pipelines for performance and cost efficiency.
• Establish and enforce data engineering standards, including coding practices, documentation, and testing strategies.
• Act as a subject matter expert on data and analytics topics, providing technical guidance to stakeholders and contributing to technical documentation and knowledge-sharing efforts.