As a Data Management Team Manager, you will be responsible for overseeing the data management operations within our organization. Your role will involve leading a team of data management professionals, ensuring the accuracy, integrity, and security of our data assets, and implementing best practices for data management processes. You will collaborate closely with other teams to support data-driven decision-making and drive improvements in data quality and efficiency.

Key Responsibilities**:

- 1. Flow Cell Leadership & Delivery Accountability

- Lead and manage a cross-functional flow cell team comprising data managers and data engineers, ensuring clear ownership of work intake through to delivery.

- Drive a flow-based delivery model , optimising work-in-progress (WIP), cycle times, and throughput across the team.

- Ensure all client deliverables are completed on time, within SLA, and to defined quality standards .

- Act as the primary escalation point for delivery risks, bottlenecks, and data-related issues impacting client outcomes.

- Maintain clear visibility of pipeline, capacity, and delivery commitments through dashboards and reporting.

- 1. Data Management Strategy & Process Governance

- Implement and continuously evolve data management strategies, policies, and procedures aligned with organisational standards and client requirements.

- Define and enforce data lifecycle processes (ingestion, validation, transformation, storage, and delivery).

- Establish robust data governance frameworks , ensuring compliance with regulatory, contractual, and internal standards.

- Maintain ownership of RAID logs for data-related risks and issues, ensuring clear documentation of business impact (e.g., revenue, delivery delays, reputational risk).

- 1. Quality, Standards & Performance Management





- Define and operationalise data quality standards, validation rules, and acceptance criteria .

- Monitor and report on KPIs and SLAs , including:

- Data quality and defect rates.

- Cycle time / lead time.

- Throughput and backlog health.

- SLA adherence and client delivery metrics.

- Implement proactive quality controls and root cause analysis processes to reduce recurring issues.

- Drive continuous improvement initiatives to enhance efficiency and reduce manual intervention.

- 1. Technical Oversight & Data Architecture Alignment

- Oversee the design, development, and maintenance of databases, pipelines, and data warehouses supporting flow cell operations.

- Ensure data solutions are scalable, secure, and aligned with enterprise architecture standards.

- Partner with engineering and platform teams to optimise tools such as DRiVS, TREX, Data Forge, or equivalent systems.

- Ensure appropriate use of automation, validation frameworks, and orchestration tools to improve data processing efficiency.

- 1. Cross-Functional Collaboration & Stakeholder Management

- Collaborate with product owners, client teams, and upstream/downstream stakeholders to define data requirements and priorities.

- Translate business needs into clear, actionable data workflows and deliverables.

- Act as a bridge between technical teams and business stakeholders, ensuring alignment on expectations, timelines, and quality.





- Provide regular updates to leadership on delivery performance, risks, and improvement initiatives.

- 1. Continuous Improvement & Flow Optimisation

- Identify and remove bottlenecks within the flow cell using data-driven insights and process analysis.

- Drive adoption of lean and flow-based principles , including:

- Work standardisation.

- Automation of repetitive tasks.

- Reduction of handoffs and rework.

- Lead process improvement initiatives focused on increasing throughput and reducing cycle times.

- Champion innovation, including AI/ML-driven opportunities for data validation, enrichment, and workflow automation.

- 1. People Leadership & Capability Development

- Mentor, coach, and develop team members, fostering a culture of accountability, collaboration, and continuous learning.

- Define clear roles and responsibilities within the flow cell, ensuring optimal skill utilisation and coverage.

- Support career development, performance management, and succession planning across data management and engineering roles.

- Promote a high-performance culture grounded in ownership, quality, and operational excellence.

- 1. Risk, Compliance & Data Security

- Ensure all data handling processes comply with relevant data protection regulations and organisational policies .

- Manage risks related to data integrity, security, and client delivery commitments.

- Implement audit-ready processes and documentation to support governance and compliance reviews.

- Success Measures (Indicative)

- Consistent SLA achievement and improved client satisfaction.

- Reduction in data defects and rework rates.

- Improved cycle time and throughput across the flow cell.

- Increased automation and reduced manual processing effort.

- Transparent,

📌 Manager, Technology Consulting (Chennai)
🏢 Merative
📍 Chennai