About the role
The Data Manager is the operational backbone of CTC's data integrity — a centralised data operations leader who brings together the quality, governance, and scalability of data across the entire delivery ecosystem, ensuring that every compliance output and insight generated by people and AI is reliable, explainable, and trusted.
Day to day, this role feels like running the nerve centre of a high-volume, high-stakes operation. You're the person who knows — in real time — whether data is clean, whether analysts are on track, whether an exception is noise or a genuine risk. You make the calls that keep delivery moving: when to release data, when to hold it, when to escalate, and when to coach an analyst through a problem rather than solving it yourself. Your decisions directly determine whether compliance outputs reach clients on time, at quality, and with the trust.
Our expectations
Role-Specific Skills
Data Operations Management
- Data Quality & Control Frameworks
- People Leadership of Technical Teams
- Root-Cause Analysis & Structured Escalation
- Cross-Functional Stakeholder Coordination
- Process Standardisation & Documentation
- Compliance Data Environment Literacy
- CTC Service-Level & Operating-Model Literacy
Technology & AI Competency
- AI Model Awareness — Understands how AI models used in data operations work, including their inputs, assumptions, and limitations, and knows when to trust, challenge, or override their outputs.
- System Signal Interpretation — Distinguishes genuine data-quality or compliance-risk signals from noise and false positives in automated alerts and exception reports.
- Technology Governance Design — Defines escalation thresholds, quality checkpoints, and rules for when human review is required versus when automation is sufficient.
- Platform Fluency (Governance Level) — Navigates Sightline, the Data Controls Engine, and workflow tools as a supervisory user — monitoring, not configuring.
- Technology-to-Business Translation — Turns data platform outputs and AI alerts into plain-language narratives that delivery and client teams can act on.
Foundational Skills
- Structured Problem-Solving
- Resilience & Composure Under Pressure
- Continuous Improvement Orientation
- Cross-Cultural Collaboration
- Commercial & Operational Awareness
Main responsibilities
What You Will Do — Core Accountabilities
Client Data Leadership
- Data Operations Mission: Own the quality, consistency, and governance of all data flowing through the compliance delivery model across clients and territories.
- Analyst Leadership & Development: Lead, coach, and develop the Data Analyst team — allocating work, reviewing quality, and building lasting capability.
- Data Readiness Governance: Ensure data is validated and certified at every delivery checkpoint before work moves forward.
- Standardised Data Processes: Define and enforce consistent processes for how data is received, checked, transformed, and reconciled.
- Exception Management & Escalation: Investigate and resolve complex data issues at root cause, escalating systemic problems early.
Quality, Controls & Assurance
- Data Controls & Audit Trails: Maintain the controls, documentation, and audit trails that underpin trusted compliance outputs.
- AI & Automation Supervision: Oversee automated and AI-driven data checks, applying human judgment where machine outputs need challenge or validation.
- Data Lineage & Explainability: Ensure every compliance output can be traced back to its source data and the logic applied to it.
Onboarding, Transition & Scale
- Onboarding & Roll-Forward: Coordinate data setup for new clients and period transitions, driving speed and repeatability.
- Cross-Territory Standardisation: Drive consistency of data practices and quality standards across all territories served.
Stakeholder Coordination & Risk
- Stakeholder Partnership: Work closely with delivery, client management, and data platform teams so data issues are surfaced early — no surprises.
- Risk & Dependency Management: Spot and mitigate data-related risks before they affect compliance timelines or quality.
Insight, Improvement & Commercial Contribution
- Operational Insight & Reporting: Produce performance insights on data quality, throughput, and automation effectiveness that drive better decisions.
- Continuous Improvement & Automation: Find recurring problems and manual workarounds and turn them into standardised, automated solutions.
- Playbook & Knowledge Codification: Build and maintain the playbooks and training materials that keep the data operations team sharp and consistent.
