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Open nowPosted 29 days ago

Data Project Manager

MyCareersFuture94,028 open roles

Pay
SGD 8,000 – SGD 9,500 a month
Where
East, Singapore
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Your applicationOpen nowData Project ManagerMyCareersFuture · East, Singapore

  1. YouYes, apply to this one.

  2. CV RocketCV written for this posting.

  3. 25 readersRecruiter, hiring manager, skeptic. Round after round.

  4. CV RocketApplied on MyCareersFuture's own form.

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The clock on this job

Early applications get read.

7.7% of postings close within 7 days. Measured by our own scanner across the market.

Share of postings closed within
  1. 1.6%1 day
  2. 3.3%3 days
  3. 7.7%7 days
  4. 14.0%14 days
  5. 33.7%30 days
This job: posted 29 days ago

The posting

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.
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