Senior Data Programs Lead

Quantum Infotech Solutions; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

Quantum Infotech Solutions in Singapore is seeking a Data Project Manager to plan and lead data initiatives across sourcing, modeling, ETL/ELT, and deployment. You will align stakeholders, manage timelines, and ensure regulatory/compliance considerations are integrated.

The role requires coordinating data governance, quality oversight, risk management, and cross-functional collaboration with data engineers, analysts, DBAs, QA, and data scientists.

Qualifications

• Experience leading data projects through lifecycle stages (planning to deployment).
• Strong stakeholder management and communication with technical and business teams.
• Hands-on with cloud data platforms (GCP/AWS) and ETL/ELT tooling; data governance focus.

Responsibilities

• Define project objectives, scope, success criteria, and deliverables.
• Coordinate data sources, owners, and access requirements.
• Lead cross-functional teams including data engineers, analysts, and data scientists.
• Oversee data quality, validation rules, and data lineage.
• Manage risks, blockers, and change requests; report to sponsors.
• Plan budgets, timelines, and resource allocation.
• Coordinate testing, UAT, and deployment activities.
• Maintain project docs, architecture diagrams, and status updates.
• Ensure post-launch support and knowledge transfer.
• Measure outcomes against KPIs (accuracy, performance, impact).

Skills

Cloud architectures

PySpark/Scala pipelines

Metadata management

Data warehousing

Scheduling platforms

Communication across teams

Tools

GCP

AWS

Airflow

Job description

Quantum Infotech Solutions in Singapore is seeking a Data Project Manager to plan and lead data initiatives across sourcing, modeling, ETL/ELT, and deployment. You will align stakeholders, manage timelines, and ensure regulatory/compliance considerations are integrated.

The role requires coordinating data governance, quality oversight, risk management, and cross-functional collaboration with data engineers, analysts, DBAs, QA, and data scientists.