Data and Batch Operations

• Bachelor’s degree or equivalent experience in Data Operations, ETL, Batch Processing, or related fields, preferably within financial services.
• Experience with commercial workload scheduling tools such as AutoSys, BMC Control‑M, or equivalent.
• Strong expertise in AWS, including Apache Airflow, supporting cloud‑based ETL and real‑time data pipelines.
• Proven ability to deliver high‑performing, high‑availability data solutions.
• Excellent verbal and written communication skills, with the ability to collaborate effectively across IT and business teams.
• Hands‑on experience with ServiceNow, including Incident, Service Request, Change, Problem, Knowledge Management, and reporting modules.
• 7-10 years of working experience is required in these areas.

Responsibilities

• Manage end‑to‑end data operations, including validation of incoming data sources, data integration and compilation, and downstream data distribution.
• Ensure best‑in‑class data quality and accuracy by defining, building, and tracking data accuracy KPIs to provide insights to Product and Data Engineering teams.
• Establish and maintain batch and ETL job lineage, ensuring compliance with governance policies and operational standards.
• Partner with Data Engineering to ensure operational controls are properly designed, implemented, and functioning effectively.
• Oversee the integration and execution of all inbound and outbound data processes.
• Identify and implement actionable improvements and optimizations to support increasing data volume, complexity, and business demand.
• Provide hands‑on support for user community training related to data usage, reporting, issue resolution, and production support.
• Monitor, triage, prioritize, and assign Data Operations work intake from ServiceNow, IT Service Desk, IT Demand processes, IT Business Partners, and other data and analytics intake channels.
• Design and implement frameworks and methodologies for deploying and scaling Data Operations solutions.
• Contribute to the vision, strategy, and roadmap for the Data Operations practice, including identifying opportunities for growth, efficiency, and modernization.
• Promote a strong culture of process documentation, data governance, operational discipline, and continuous improvement.
• Develop and integrate operational reports, KPI dashboards, and metrics packages to support technology leadership and decision‑making.
  • Evaluate existing ETL platforms and identify alternative solutions when current hardware or software cannot efficiently support large‑scale data processing requirements.