Senior Data Manager

TECHEMERGE SOLUTIONS PTE. LTD.; Singapore; Presencial; SGD 90.000 - 130.000; Jornada completa

Descripción de la vacante

TECHEMERGE SOLUTIONS PTE. LTD. in Singapore is seeking a Data Project Manager to lead cross-functional data initiatives.

You will define objectives, manage timelines, and coordinate stakeholders across business, data engineering, and leadership to ensure on-time delivery. The role focuses on data governance, quality oversight, and ensuring successful deployment of data pipelines using cloud technologies. Strong communication and Agile experience are essential for coordinating multiple teams and

Formación

• Experience leading data-driven projects across business and IT.
• Proven ability to translate requirements into technical specs.
• Strong knowledge of data governance and privacy.
• Experience with Agile/Scrum or Kanban workflows.
• Familiarity with data quality and validation processes.
• Excellent stakeholder management skills.

Responsabilidades

• Define project objectives, scope, and success criteria with stakeholders.
• Break work into phases: requirements to deployment.
• Estimate timelines, budget, and resources.
• Coordinate data sources, ownership, and access.
• Liaise between business teams, engineers, and leadership.
• Translate business needs into technical specs.
• Manage risks, blockers, and change requests.
• Oversee testing plans: data validation and end-to-end pipelines.
• Maintain documentation and status reporting.
• Support post-launch training and adoption.

Conocimientos

Cloud data architectures

PySpark data pipelines

Metadata management

Data warehousing

Scheduling platforms

Communication between teams

Descripción del empleo

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