Team Leader, Process Automation and Data Operations, Company Financials Data, Pune

Lead, coach, and develop a team of data professionals, fostering accountability, collaboration, and continuous improvement.

Drive operational excellence by monitoring queue health, throughput, operational metrics, quality indicators, timeliness and exception trends using data-driven insights to optimize performance and ensure accurate, efficient and consistent delivery.

Establish operational governance through dashboards, quality controls, standard operating procedures, documentation, training and escalation processes that strengthen workflow visibility, issue detection, production oversight and consistent execution.

Design, implement, and continuously enhance scalable automation solutions that reduce manual effort, improves operational efficiency and enable AI-ready, governed workflows and structured data outputs.

Partner with Workflow, Product, Engineering, vendors, and other stakeholders to implement and scale workflow and automation enhancements.

Bachelor’s degree (or higher) in a STEM field such as Computer Science, Engineering, Data Science, Mathematics, Statistics, Operations Research, or equivalent practical experience

5+ years of experience in Data Operations, Process Improvement, Automation, Industrial Engineering, Operations Research, or a related field, with 3+ years of people leadership experience

Strong technical proficiency in SQL, Python, Excel, ETL/ELT pipelines, data transformation workflows, and data visualization or workflow monitoring tools (e.g., Qlik Sense, Power BI), with knowledge of data observability, data quality monitoring, and production controls

Experience driving operational excellence by improving workflows, reducing manual effort, simplifying processes, and applying data management best practices, including data quality, process controls, operational metrics, issue management, and workflow documentation

Strong analytical and problem-solving skills, with experience leveraging operational dashboards and metrics (e.g., throughput, SLA performance, defect rates, queue aging, rework, and productivity) to identify trends, root causes, bottlenecks, and continuous improvement opportunities