Position: Manager – Data & Research Operations

Department: Research / Data Operations

Experience: 5–8 years

Reporting To: Head – Data / Research / Business Operations

Team: Research & Data Operations Team

Role Overview

We are looking for a Manager – Data & Research Operations to lead a team responsible for data research, collection, management, validation, formatting, quality and governance.

The role will be responsible for ensuring that business data is accurate, clean, structured, reliable and usable, while continuously improving data processes through automation, AI and technology.

The ideal candidate should have robust experience in data operations, data governance, data hygiene, research, data accuracy, team management and process automation, along with a practical understanding of AI tools and their application in data workflows.

Key Responsibilities

1. Data Operations & Management

- Lead day-to-day data research, collection, processing and management activities.
- Define and maintain processes for data entry, validation, cleansing, formatting and enrichment.
- Ensure data is properly structured and maintained across databases, spreadsheets, CRM/HRMS and other systems.
- Develop standardized data formats, templates and operating procedures.

2. Data Quality & Accuracy

- Establish processes to maintain high levels of data accuracy, completeness, consistency and reliability.
- Conduct regular data audits and identify duplicate, missing, outdated or incorrect information.
- Develop data-quality checks and validation mechanisms.
- Track data-quality metrics and drive corrective actions.

3. Data Governance

- Establish and maintain data governance standards, policies and SOPs.
- Define data ownership, data definitions, validation rules and quality standards.
- Ensure appropriate controls around data access, usage, storage and maintenance.
- Maintain documentation and data dictionaries where required.

4. Research & Data Intelligence





- Lead the research team in gathering information from internal and external sources.
- Ensure research outputs are accurate, structured and supported by reliable sources.
- Convert unstructured information into usable datasets and reports.
- Work with business stakeholders to understand data requirements and deliver relevant insights.

5. Team Management

- Manage, mentor and develop the Research & Data Operations team.
- Allocate work, establish daily/weekly targets and monitor productivity.
- Review team output for accuracy and quality.
- Conduct performance reviews and provide coaching and feedback.
- Build a culture of ownership, attention to detail and continuous improvement.

6. AI & Automation

- Identify repetitive, manual and time-consuming data processes that can be automated.
- Explore and implement AI-based solutions for data collection, cleaning, classification, formatting, validation and research.
- Use AI tools to improve research productivity and data-processing efficiency.
- Work with technology/IT teams to implement automation workflows.
- Establish appropriate human-review mechanisms to ensure AI-generated outputs are accurate.

7. Process Improvement

- Identify gaps and inefficiencies in existing data and research processes.
- Develop dashboards, trackers and MIS for monitoring data quality and team performance.
- Establish measurable KPIs for accuracy, productivity, turnaround time and data completeness.
- Continuously improve workflows through technology, automation and standardization.

Required Skills & Experience

- 5–8 years of experience in Data Operations, Data Management, Research Operations,



Data Governance, Business Intelligence or a related field.
- Experience managing and leading a team.
- Strong understanding of data hygiene, data quality and data accuracy.
- Experience in data validation, cleansing, formatting, enrichment and standardization.
- Knowledge of data governance principles, SOPs and data management frameworks.
- Strong Excel/Google Sheets skills; knowledge of databases, CRM/ERP or BI tools is preferred.
- Experience handling large datasets and maintaining structured databases.
- Practical knowledge of AI tools and AI-assisted workflows.
- Understanding of automation tools and the ability to identify opportunities for process automation.
- Strong analytical, problem-solving and research skills.
- Excellent attention to detail and ability to identify inconsistencies in data.
- Strong communication and stakeholder-management skills.

Preferred Skills

- Advanced Excel, Power Query or similar data-processing tools.
- SQL or basic database knowledge.
- Power BI/Tableau or other visualization tools.
- Experience with AI tools such as ChatGPT, Gemini, Claude or similar platforms.
- Exposure to no-code/low-code automation platforms.
- Experience creating data-quality dashboards and MIS.
- Understanding of APIs and automated data workflows would be an advantage.

Key Performance Indicators (KPIs)

- Data accuracy and error rate
- Data completeness
- Duplicate/error reduction
- Data validation turnaround time
- Research productivity and turnaround time
- Team productivity
- Compliance with data governance standards
- Successful automation of manual processes
- Reduction in manual data-processing time
- Quality and reliability of research outputs
- Stakeholder satisfaction

Ideal Candidate The ideal candidate is someone who can manage people & data, understand data, improve processes and leverage AI—rather than being limited to traditional data-entry or research activities.

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