Senior Data Manager

TechEmerge Solutions; Singapore; On-site; SGD 120,000 - 190,000; Full time

Job summary

TechEmerge Solutions in Singapore is seeking a data project manager to lead complex data initiatives from planning through deployment. You will define objectives, coordinate across data engineers, analysts, and leadership, and ensure on-time delivery within budget.

You will establish data governance, track risks, manage scope changes, and oversee QA, UAT, and deployment; strong communication between business and technical teams is essential.

Qualifications

• 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

Responsibilities

• 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.
• 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.
• 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).
• 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.
• 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).
• Track costs.
• Manage vendor and contractor relationships if external data tools or consultants are used.
• Ensure testing plans cover data validation, transformation logic, and end-to-end pipeline testing.
• Coordinate UAT (User Acceptance Testing) with business stakeholders.
• Maintain project documentation, including requirements, data dictionaries, architecture diagrams, and status reports.
• Ensure knowledge transfer and handover documentation for ongoing maintenance.
• 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.
• Conduct post-mortems and retrospectives to capture lessons learned.
• Measure project outcomes against KPIs (data accuracy, system performance, and business impact).

Skills

Cloud GCP/AWS

PySpark/Scala pipelines

Data warehousing

Metadata management

Communication skills

Tools

Scheduling platforms

Job description

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