Data Project Manager

INFINITE COMPUTER SOLUTIONS PTE LTD; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

INFINITE COMPUTER SOLUTIONS PTE LTD is seeking a skilled Project Manager to lead data-centric initiatives in Singapore. You will plan and scope projects, define objectives, and collaborate with stakeholders to deliver measurable outcomes.

The role involves coordinating data engineers, analysts, DBAs, and governance teams, managing risks, budgets, and timelines, and ensuring data quality and regulatory compliance throughout the project lifecycle.

Qualifications

• Technical fluency in cloud architectures (GCP/AWS) and data pipelines.
• Ability to communicate effectively between technical and business teams and make informed trade-offs.

Responsibilities

• Define project objectives, deliverables, and success criteria 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.
• Liaise between business teams, data engineers, analysts, data scientists, and leadership.
• 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.
• Coordinate cross-functional dependencies (IT, security, and compliance).
• Ensure data quality standards, validation rules, and cleansing processes.
• Track data lineage and documentation.

Skills

Cloud architectures (GCP/AWS)

PySpark/Scala pipelines

Metadata management

Data warehousing

Scheduling platforms

Communication skills

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