Position: Manager / Lead – Data Strategy & Operations

Role Overview The role is responsible for developing and executing the company-wide data strategy, ensuring data quality and governance, improving research and data-processing operations, and driving automation and AI-led process improvements.

The position will lead multiple teams through Team Leaders, establish robust data-quality and governance frameworks, optimize operational workflows, and work closely with Leadership, Product, Programming, Sales, and Operations teams to improve data accuracy, scalability, productivity, and business value.

Key Skills & Experience Required

- Experience in Data Strategy, Data Operations, Data Governance, Research Operations, or Data Enrichment.
- Experience managing multiple teams through Team Leaders.
- Strong understanding of data integrity, quality controls, data governance, and process management.
- Proven track record of improving data-processing speed, quality, efficiency, and scalability.
- Experience implementing workflow automation, AI solutions, or AI-enabled processes.
- Strong skills in Excel, data analysis, structured research, communication, and problem-solving.
- Strong attention to processes, protocols, accuracy, quality, and accountability.
- Strong cross-functional communication, stakeholder management, and leadership skills.

Key Responsibilities 1. Data Strategy & Governance

- Define and execute the company-wide data strategy and roadmap.
- Establish and enforce data-quality standards, governance protocols, SOPs, and controls.
- Ensure data accuracy, completeness, consistency, freshness, security, and usability.
- Identify and add valuable data sources, records, and data points.
- Create standardized data definitions, structures, taxonomies, and validation rules.
- Establish and maintain a reliable Single Source of Truth (SSOT) and reduce duplicate, inconsistent, or conflicting data.

2. Team & Operations Management

- Lead the SLM Research, POS Research, and Formatting Team Leaders.
- Set and monitor quality, output, turnaround-time, and productivity targets.
- Allocate resources based on workload, priorities, and business requirements.
- Coach Team Leaders and develop high-potential employees.
- Manage underperformance through structured performance improvement and development plans.




- Ensure appropriate succession coverage and leadership readiness within the teams.

3. Process Improvement & Operational Excellence

- Analyze existing workflows and identify inefficient, redundant, or unnecessary steps.
- Improve processing speed, productivity, quality, and scalability through process redesign.
- Reduce manual work through automation, workflow optimization, and AI.
- Define requirements, objectives, and success criteria for data and automation projects.
- Test, validate, and approve process improvements before full implementation.
- Conduct root-cause analysis and resolve recurring data-quality and operational issues.
- Maintain updated SOPs, training materials, audit controls, and escalation procedures.

4. Data Quality & Research Operations

- Monitor data quality across research, formatting, enrichment, and processing activities.
- Establish appropriate validation mechanisms to identify and prevent data errors.
- Ensure data-processing activities meet defined quality, accuracy, and turnaround-time standards.
- Identify opportunities to expand and improve data sources and datasets.
- Establish controls to minimize duplicate, incomplete, outdated, or inconsistent data.

5. Automation & AI

- Identify opportunities where automation and AI can improve operational efficiency and data quality.
- Partner with Product and Programming teams to implement AI-enabled workflows.
- Define requirements and expected outcomes for AI and automation initiatives.
- Ensure AI-generated outputs meet defined standards for accuracy, traceability, privacy, and quality.
- Test and monitor AI workflows before and after implementation.
- Continuously identify opportunities to reduce manual intervention and improve scalability.

6. Cross-Functional Collaboration

- Partner with Leadership, Product, Programming, Sales, and Operations teams.
- Understand business requirements and translate them into actionable data and operational solutions.




- Communicate data health, operational risks, challenges, and improvement initiatives to senior leadership.
- Support cross-functional projects involving data, automation, research, and process improvement.

7. Reporting & Performance Management

- Monitor individual and team-level performance against defined KPIs.
- Track processing volume, turnaround time, accuracy, error rates, and productivity.
- Report data health, operational risks, and improvement progress to senior leadership.
- Identify performance gaps and implement corrective actions.
- Monitor training effectiveness and employee development.
- Build a pipeline of promotion-ready employees and future Team Leaders.

Key Performance Metrics / KPIs

Performance Area

Key Metric

Productivity

Processing volume and turnaround time by individual and team

Data Quality

Data accuracy and error rate by individual and team

Operational Efficiency

Reduction in processing time and manual effort

Process Improvement

Number and impact of process improvements implemented

Automation & AI

Reduction in manual processes through automation/AI

Training & Development

Training completion and assessment results

Talent Development

Promotion-ready employees and succession coverage

Performance Management

Underperformer improvement or transition rate

Governance

Compliance with SOPs, data-quality standards, and audit controls

Data Health

Accuracy, completeness, consistency, freshness, and usability of data

Scalability

Ability to increase processing capacity without compromising quality

Stakeholder Management

Timely resolution of cross-functional data and operational issues

Key Success Factors

Success in this role will be measured by the ability to:

- Build a scalable and reliable data operation.
- Improve data quality, accuracy, consistency, and usability.
- Increase operational speed and productivity.
- Reduce manual work through automation and AI.
- Build strong Team Leaders and second-line leadership.
- Establish effective data governance and process controls.
- Create measurable improvements in quality, cost, turnaround time, and scalability.
- Build solid collaboration between Data, Product, Programming, Sales, Operations, and Leadership teams.

📌 Manager research (New Delhi)
🏢 Interlynx Systems
📍 New Delhi