Data Operations Manager, Digital Finance

Role Overview

The Data Operations Manager in Digital Finance will lead the operational lifecycle of finance data products, ensuring accuracy, scalability, and seamless collaboration across technical and business teams.

What You Will Do

Responsibilities include serving as the primary liaison across various stakeholders, leading and supporting end-to-end operational activities of enterprise Finance data lake, lakehouse, and warehouse products, and executing and progressively automating reconciliations, control checks, and data-quality rules.

Why It Might Be a Fit

The ideal candidate will have a strong grasp of modern data architectures, experience with T-SQL, data warehouses, and ETL pipelines, and foundational GenAI and AI-agent fluency.

Requirements

• Bachelor’s or master’s degree in computer science, Engineering, Data Science, or a related field
• Typically 2-3 years of experience in product operations, ideally within data platforms and Agile environments
• Proven ability to manage timelines, resources, and deliverables across complex data product lifecycles
• Strong stakeholder engagement skills, translating business needs into actionable operations solution requirements
• Experience coordinating cross-functional teams and managing scopes with stakeholders and technical partners
• Familiarity with finance domain processes and systems; understanding of financial data flows is a plus
• Solid grasp of modern data architectures including Data Lake, Lakehouse, and Data Mesh
• Experience with T-SQL, data warehouses (e.g., Snowflake, Databricks), and ETL pipelines; understanding of BI tools and data visualization concepts is a plus
• Foundational GenAI and AI-agent fluency, including safe use of approved tools, instruction quality, human review, data privacy and escalation of unexpected outputs

Benefits

• Insurance plans
• Retirement plans
• Wellbeing resources
• Global recognition programs
• Flexible and hybrid working options
• Minimum 14 weeks paid parental leave

Tagged as: Data Visualization, Snowflake, SQL