Senior Data Manager

Quantum Infotech Solutions; Singapore; Sur place; SGD 120 000 - 180 000; Plein temps

Résumé du poste

Quantum Infotech Solutions in Singapore is seeking a Data Project Manager to plan and lead data initiatives across sourcing, modeling, ETL/ELT, and deployment. You will align stakeholders, manage timelines, and ensure regulatory/compliance considerations are integrated.

The role requires coordinating data governance, quality oversight, risk management, and cross-functional collaboration with data engineers, analysts, DBAs, QA, and data scientists.

Qualifications

• Experience leading data projects through lifecycle stages (planning to deployment).
• Strong stakeholder management and communication with technical and business teams.
• Hands-on with cloud data platforms (GCP/AWS) and ETL/ELT tooling; data governance focus.

Responsabilités

• Define project objectives, scope, success criteria, and deliverables.
• Coordinate data sources, owners, and access requirements.
• Lead cross-functional teams including data engineers, analysts, and data scientists.
• Oversee data quality, validation rules, and data lineage.
• Manage risks, blockers, and change requests; report to sponsors.
• Plan budgets, timelines, and resource allocation.
• Coordinate testing, UAT, and deployment activities.
• Maintain project docs, architecture diagrams, and status updates.
• Ensure post-launch support and knowledge transfer.
• Measure outcomes against KPIs (accuracy, performance, impact).

Connaissances

Cloud architectures

PySpark/Scala pipelines

Metadata management

Data warehousing

Scheduling platforms

Communication across teams

Outils

GCP

AWS

Airflow

Description du poste

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