Data Project Manager

infinite Computer Solution; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

infinite Computer Solution is seeking a Data Project Manager in Singapore to drive planning, scoping, and governance of data initiatives. You will coordinate data engineers, analysts, and stakeholders to deliver end-to-end pipelines and ensure quality and regulatory compliance.

The role emphasizes cross-functional collaboration, effective communication, and continuous improvement through testing, UAT, and post-launch evaluation.

Qualifications

• Experience leading data projects with cross-functional teams.
• Strong knowledge of cloud data platforms (GCP/AWS) and data pipelines.
• Ability to translate business requirements into technical specs.

Responsibilities

• Define project objectives, scope, deliverables, and success criteria with stakeholders.
• Break down work into phases: requirements, data sourcing, modeling, ETL/ELT, validation, deployment.
• Coordinate with data engineers, analysts, DBAs, QA testers, and data scientists.
• Ensure data quality, governance, privacy, and regulatory compliance.
• Track risks, issues, and dependencies; maintain risk register and mitigation plans.
• Monitor budgets, vendor/contractor relationships, and resource utilization.
• Oversee testing plans incl. data validation and end-to-end pipeline testing; coordinate UAT.
• Maintain project documentation, data dictionaries, and architecture diagrams.
• Oversee rollout, change management, and user training post-launch.
• Conduct post-mortems and measure outcomes against KPIs (data accuracy, performance).

Skills

Cloud architectures

GCP/AWS

PySpark/Scala

Metadata management

Data warehousing

Scheduling platforms

Stakeholder communication

Tools

Airflow

Job description

Key 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 effective between technical and business teams and make informed trade-off decisions.