Senior Data Manager

quantum infotech solutions pte. ltd.; Singapore; On-site; SGD 90,000 - 160,000; Full time

Job summary

Quantum Infotech Solutions Pte. Ltd. seeks an experienced Data Projects Lead to drive planning, scoping, and delivery of complex data initiatives.

You will coordinate with business teams and technical squads to align requirements with data pipelines, governance, and deployment, ensuring on-time, on-budget outcomes and rigorous data quality.

Qualifications

• Proven ability to translate business requirements into technical specifications.

Responsibilities

• Define project objectives, scope, and success criteria with stakeholders.

Skills

Cloud architectures

PySpark/Scala data pipelines

Metadata management

Data warehousing

Scheduling platforms

Communication

Trade-off decisions

Job description

1. Planning & Scoping:

• Define project objectives, deliverables, and success criteria in collaboration with stakeholders.
• Break down work into phases (requirements, data sourcing, modeling, ETL/ELT, validation, and deployment).
• Estimate timelines, budget, and resource requirements.
• Identify data sources, ownership, and access requirements early.

2. Stakeholder Management:

• Act as the primary liaison between business teams, data engineers, analysts, data scientists, and leadership.
• Gather and translate business requirements into technical specifications.
• Manage expectations around data quality, timelines, and scope changes.
• Report progress, risks, and blockers to sponsors and steering committees.

3. Team Coordination:

• Assign tasks across data engineers, analysts, DBAs, QA testers, and data scientists.
• Facilitate stand-ups, sprint planning, and retrospectives (often Agile/Scrum or Kanban).
• Resolve cross-functional dependencies (e.g., IT infrastructure, security, and compliance teams).

4. Data Governance & Quality Oversight:

• Ensure data quality standards, validation rules, and cleansing processes are followed.
• Coordinate with governance and compliance teams on privacy, security, and regulatory requirements.
• Track data lineage and documentation requirements.

5. Risk & Issue Management:

• Identify risks specific to data projects, including data quality issues, schema changes, integration failures, and scalability concerns.
• Maintain a risk register and mitigation plans.
• Escalate blockers (e.g., missing access, vendor delays, and infrastructure limitations).

6. P&L, Budget Tracking &Resource Management:

• Track costs.
• Manage vendor and contractor relationships if external data tools or consultants are used.

7. Quality Assurance & Testing Oversight:

• Ensure testing plans cover data validation, transformation logic, and end-to-end pipeline testing.
• Coordinate UAT (User Acceptance Testing) with business stakeholders.

8. Documentation & Reporting

• Maintain project documentation, including requirements, data dictionaries, architecture diagrams, and status reports.
• Ensure knowledge transfer and handover documentation for ongoing maintenance.

9. Deployment & Change Management

• Oversee rollout and migration plans, including rollback strategies.
• Manage change requests and their impact on scope and timelines.
• Support user training and adoption post-launch.

10. Post-Project Evaluation

• Conduct post-mortems and retrospectives to capture lessons learned.
• Measure project outcomes against KPIs (data accuracy, system performance, and business impact).

Required Skills

• Technical fluency in Cloud architectures (GCP/AWS), PySpark/Scala data pipelines, metadata management, data warehousing, and scheduling platforms.
• Ability to communicate effectively between technical and business teams and make informed trade-off decisions