Junior Data Project Manager

ENCORA TECHNOLOGIES PTE. LTD.; Singapore; On-site; SGD 60,000 - 100,000; Part time

Job summary

ENCORA TECHNOLOGIES PTE. LTD. is seeking a data project manager for a 6‑month contract (extendable) to lead data initiatives across planning, sourcing, modeling, ETL/ELT, validation, and deployment.

You will coordinate stakeholders, manage data governance and quality, and guide cross‑functional teams through Agile/Scrum processes, ensuring on‑time delivery and clear reporting.

Strong cloud (GCP/AWS), Spark pipelines, metadata management, and data warehousing experience are essential.

Qualifications

• Experience with cloud data architectures and Spark-based pipelines.
• Strong communication between technical and business teams.

Responsibilities

• Plan and scope data projects with stakeholders.
• Coordinate cross-functional teams and Agile/Scrum ceremonies.
• Oversee data governance, quality, and privacy requirements.
• Manage risks, budgets, and vendor relationships.
• Document requirements, architecture, and status reporting.
• Coordinate deployment, change management, and UAT.
• Measure outcomes against KPIs such as data accuracy and system performance.

Skills

GCP/AWS

PySpark/Scala

Metadata management

Data warehousing

Scheduling platforms

Stakeholder communication

Job description

Contract Duration:

6 Months (Extendable)

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