Data Project Manager

QUESS SELECTION & SERVICES PTE. LTD.; Singapore; On-site; SGD 90,000 - 130,000; Full time

Job summary

QUESS SELECTION & SERVICES PTE. LTD. is seeking a Senior Data Project Lead in Singapore to define project objectives, manage data governance, and coordinate cross-functional teams across data engineers, analysts, and data scientists.

You will oversee risk management, budgeting, and deployment plans, ensuring data quality and timely delivery in collaboration with stakeholders across the business. Fluency in Agile/Scrum is a plus.

Qualifications

• Experience in cloud data architectures and pipelines.
• Ability to translate business requirements into technical specs.
• Strong data governance and quality oversight.
• Experience coordinating cross-functional teams.
• Familiarity with Agile/Scrum or Kanban.
• Experience with risk management and budgeting.

Responsibilities

• Define project objectives, deliverables, and success criteria with stakeholders.
• Act as liaison between business, data engineers, analysts, data scientists, and leadership.
• Assign tasks across teams and facilitate stand-ups and sprints.
• Ensure data quality and governance through validation rules.
• Manage risks, blockers, and escalation to sponsors.
• Monitor costs, vendor relationships, and resource management.
• Oversee testing, UAT coordination, and deployment plans.
• Maintain documentation and status reporting.

Skills

Cloud architectures

PySpark/Scala

Data pipelines

Data warehousing

Metadata management

Stakeholder communication

Agile/Scrum Kanban

Job description

Job Description & Responsibilities

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.