Data Project Manager(Clients IBM, DBS)

NEXTERA SOFTWARE SERVICES PTE. LTD.; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

NEXTERA SOFTWARE SERVICES PTE. LTD. is seeking an experienced Data Project Manager to lead complex data initiatives in a fast-paced environment. You will coordinate cross-functional teams spanning data engineers, analysts, and data scientists to deliver end-to-end data solutions.

The role emphasizes planning, governance, risk management, and stakeholder communication, with a focus on data quality, security, and regulatory considerations. Singapore-based, on-site collaboration is expected.

Qualifications

• Senior PM with 9+ years of experience in data project management.
• Proven ability to plan, scope, and deliver data projects end-to-end.
• Strong stakeholder management and cross-functional coordination.

Responsibilities

• Define project objectives, deliverables and success criteria with stakeholders.
• Break down work into phases: requirements, data sourcing, modeling, ETL/ELT, validation, deployment.
• Estimate timelines, budget and resource needs.

Skills

Cloud architecture (GCP/AWS)

PySpark/Scala pipelines

Metadata management

Data warehousing

Scheduling & planning

Job description

JD : DATA Project Manager

1. Sr PM – 9+ years of experience

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, deployment)

Estimate timelines, budget,and resource needs

Identify data sources,ownership, and access requirements early

2. Stakeholder Management

Act as the primary liaisonbetween business teams, data engineers, analysts, data scientists, andleadership

Gather and translate businessrequirements into technical specifications

Manage expectations around data quality, timelines, and scope changes

Report progress, risks, andblockers to sponsors and steering committees

3. Team Coordination

Assign tasks across dataengineers, analysts, DBAs, QA/testers, and data scientists

Facilitate standups, sprintplanning, and retrospectives (often Agile/Scrum or Kanban)

Resolve cross-functionaldependencies (e.g., IT infrastructure, security, compliance teams)

4. Data Governance &Quality Oversight

Ensure data qualitystandards, validation rules, and cleansing processes are followed

Coordinate withgovernance/compliance teams on privacy, security, and regulatory requirements

Track data lineage anddocumentation requirements

5. Risk & IssueManagement

Identify risks specific todata projects: data quality issues, schema changes, integration failures,scalability concerns

Maintain a risk register andmitigation plans

Escalate blockers (e.g.,missing access, vendor delays, infrastructure limits)

6. P&L - Budgettracking & Resource Management

Track costs

Manage vendor/contractorrelationships if external data tools or consultants are used

7. Quality Assurance &Testing Oversight

Ensure testing plans coverdata validation, transformation logic, and end-to-end pipeline testing

Coordinate UAT (useracceptance testing) with business stakeholders

8. Documentation &Reporting

Maintain projectdocumentation: requirements, data dictionaries, architecture diagrams, statusreports

Ensure knowledge transfer andhandover documentation for ongoing maintenance

9. Deployment & ChangeManagement

Oversee rollout/migration plans, including rollback strategies

Manage change requests and their impact on scope/timeline

Support user training and adoption post-launch

10. Post-ProjectEvaluation

Conductpost-mortems/retrospectives to capture lessons learned

Measure project outcomesagainst KPIs (data accuracy, system performance, business impact)

Required Skills

Have technical fluency to understand Cloud architecture(GCP/AWS), Pyspark/Scala data pipelines, Metadatamanagement, Datawarehousing, Scheduling

To communicate effectivelybetween technical and business teams and to make informed tradeoff decisions.