Project Data Manager

Responsibilities

1. Overall Delivery Management: Take end-to-end ownership of data project delivery, including project planning, milestones, resource coordination, risk management, issue tracking, change management, and client reporting.
2. Cross-Functional Collaboration: Collaborate closely with client business teams, IT teams, data teams, and internal engineers to drive requirement clarification, scope confirmation, priority management, and delivery acceptance.
3. Technical Comprehension: Understand and track key technical components in data projects, including ETL/ELT pipelines, data modeling, data warehouses, data lakes, orchestration/scheduling, data quality, access control, data governance, and cutover.
4. Task Decomposition & Risk Identification: Assist the team in breaking down project tasks while identifying technical, data, environment, and cross-team collaboration risks.
5. Meeting Facilitation: Organize and drive project meetings, including kick-offs, requirement discussions, sprint/weekly reviews, risk sessions, UAT, go-live preparation, and post-project retrospectives.
6. Documentation Management: Manage project documentation and deliverables, including project plans, RACI matrices, risk/issue logs, status reports, requirement specifications, test plans, UAT records, go-live plans, and sign-off materials.
7. Progress & Quality Tracking: Monitor project progress and delivery quality, proactively identify delays, scope creep, requirement changes, data quality issues, and communication risks, and drive solutions to closure.
8. Bridge Communication: Establish effective communication between clients and technical teams, translating business requirements into clear technical delivery tasks, and tech issues into business-impact, risk, and decision items for clients.
9. Pre-sales & Proposal Support: Support pre-sales or early-stage project assessments, including preliminary workload estimation, delivery plan design, project risk assessment, and implementation solution recommendations.
10. Methodology Refinement: Drive project retrospectives and delivery methodology documentation, continuously optimizing the company's delivery processes, templates, and best practices for data projects.

Requirements

1. Experience: 3+ years of experience in project management, technical delivery, data project implementation, BI/data analysis, or data engineering.
2. Project Background: Participated in at least one or more of the following: ETL/ELT, Data Warehouse, Data Lake, BI reporting, data migration, data governance, data quality, cloud data platform construction, or enterprise data integration projects.
3. Technical Foundation: Possess a foundational understanding of data technologies, capable of comprehending SQL, database schemas, data mapping, data pipelines, scheduling dependencies, data quality checks, and upstream/downstream system relationships.
4. Collaboration: Ability to collaborate efficiently with Data Engineers, BI Developers, Solution Architects, Business Analysts, and Data Governance Consultants.
5. Methodologies: Familiar with standard project management methodologies, including project planning, scope management, risk management, issue management, change management, resource coordination, and client communication.
6. Communication & Stakeholder Management: Strong client-facing communication skills to facilitate meetings, drive decisions, handle conflicts, and clearly report project status, risks, and next steps.
7. Problem-Solving: Strong analytical and task-decomposition skills to identify critical paths, bottlenecks, and priorities in complex projects.
8. Language Skills: Excellent written and verbal communication skills in both Chinese and English, with the ability to author project documentation, meeting minutes, status reports, and delivery materials.
9. Work Ethic: Highly proactive, accountable, and capable of driving execution in multi-project, multi-team, and multi-stakeholder environments.

Preferred Qualifications

1. Background: Previous experience as a Data Engineer, BI Developer, Analytics Engineer, Technical BA, Solution Consultant, or Implementation Consultant.
2. Tech Stack: Familiar with one or more data/cloud platforms or tools, such as Snowflake, Databricks, BigQuery, Redshift, Azure Synapse, Airflow, dbt, Fivetran, Informatica, Talend, Power BI, Tableau, etc.
3. Governance Experience: Experience in data governance, data quality, master data management (MDM), data catalogs, data lineage, or access control projects.
4. Industry Experience: Experience working in consulting firms, system integrators, SaaS implementations, or enterprise-level IT project delivery.
5. Certifications: Holder of PMP, Prince2, Scrum Master, Product Owner, or relevant Databricks, Snowflake, AWS, Azure, or GCP certifications.
6. Pre-sales Experience: Experience in pre-sales support, solution design, workload estimation, or client workshops.

Benefit:

• Medical insurance
• Annual leave
• MPF
• Sick leave