This role is for one of the Weekday's clients

Min Experience: 3+ years

Location: Bengaluru, Karnataka, India, India
JobType: full-time

We are looking for a highly analytical and detail-oriented Senior Manager – Data Operations to lead and optimize data-driven operational processes across the organization. This role is ideal for professionals who enjoy working with large datasets, improving operational efficiency, and enabling business decisions through accurate data management and reporting. You will collaborate with cross-functional teams to ensure data quality, streamline workflows, develop operational dashboards, and support business planning initiatives.

The ideal candidate should possess strong problem-solving abilities, experience in data operations, process optimization, stakeholder management, and operational analytics. Exposure to demand and supply planning is considered a valuable advantage.

Requirements

Key Responsibilities

• Manage and optimize day-to-day data operations to ensure data accuracy, consistency, and reliability.
• Design, implement, and improve operational workflows that enhance productivity and reduce manual effort.
• Monitor data pipelines, identify operational bottlenecks, and drive process improvements.
• Develop dashboards, reports, and KPIs that provide actionable insights to business stakeholders.
• Perform regular data validation, cleansing, reconciliation, and quality assurance activities.
• Collaborate with business, operations, product, and engineering teams to understand data requirements and implement scalable solutions.
• Analyze operational trends and generate recommendations to improve business performance.
• Maintain documentation for operational processes, data standards, and governance practices.
• Support automation initiatives by identifying repetitive tasks and working with technical teams to automate workflows.
• Track operational metrics and prepare periodic management reports.
• Ensure adherence to data governance, compliance, and security standards.
• Participate in cross-functional projects involving data migration, system enhancements, and operational transformation.
• Mentor junior team members and promote best practices in data operations and process management.

Required Skills

• Strong understanding of data operations, data lifecycle management, and operational processes.
• Experience working with large datasets and maintaining high standards of data quality.
• Proficiency in Microsoft Excel, including advanced functions, Pivot Tables, Power Query, and data analysis techniques.
• Hands-on experience with SQL for querying, validation, and reporting.
• Familiarity with business intelligence and visualization tools such as Power BI, Tableau, or Looker.
• Strong analytical thinking with the ability to identify trends, anomalies, and optimization opportunities.
• Experience creating operational dashboards and KPI reporting.
• Excellent problem-solving and root cause analysis skills.
• Strong communication and stakeholder management abilities.
• Ability to manage multiple priorities in a fast-paced environment.
• Knowledge of data governance, documentation, and operational best practices.

Good-to-Have Skills

• Experience with Demand and Supply Planning processes.
• Exposure to inventory planning, forecasting, or supply chain analytics.
• Knowledge of ERP platforms such as SAP, Oracle, Microsoft Dynamics, or similar enterprise systems.
• Familiarity with Python or R for data analysis and automation.
• Understanding of workflow automation tools and ETL processes.
• Experience working in operations, retail, e-commerce, manufacturing, logistics, or supply chain environments.
• Exposure to predictive analytics or forecasting models.

Qualifications

• Bachelor's degree in Engineering, Computer Science, Information Technology, Statistics, Mathematics, Business Analytics, Operations Management, or a related discipline.
• 3–7 years of experience in Data Operations, Business Operations, Operations Analytics, MIS, Data Management, or a related field.
• Experience working in data-intensive environments with cross-functional collaboration is highly preferred.