Data Project Manager

Essential Duties

1. Project Planning & Scoping

• Collaborate with stakeholders to establish project objectives, deliverables, and criteria for success.
• Decompose work into distinct phases, including requirements gathering, data sourcing, modeling, ETL/ELT processes, validation, and deployment.
• Estimate project timelines, budgets, and resource allocations.
• Identify data sources, ownership, and access requirements from the outset.

2. Stakeholder Engagement

• Serve as the main point of contact among business teams, data engineers, analysts, data scientists, and leadership.
• Collect and convert business needs into technical specifications.
• Manage expectations related to data quality, timelines, and adjustments to scope.
• Provide updates on progress, risks, and obstacles to sponsors and steering committees.

3. Team Management

• Distribute tasks among data engineers, analysts, DBAs, QA testers, and data scientists.
• Facilitate stand-up meetings, sprint planning, and retrospectives (utilizing Agile/Scrum or Kanban methodologies).
• Address cross-functional dependencies, such as those involving IT infrastructure, security, and compliance teams.

4. Data Governance & Quality Management

• Ensure adherence to data quality standards, validation rules, and cleansing procedures.
• Collaborate with governance and compliance teams to meet privacy, security, and regulatory standards.
• Monitor data lineage and documentation needs.

5. Risk & Issue Management

• Identify risks associated with data projects, such as data quality challenges, schema modifications, integration failures, and scalability issues.
• Maintain a risk register and develop mitigation strategies.
• Raise blockers, including access issues, vendor delays, and infrastructure constraints.

6. Profit & Loss, Budget Monitoring & Resource Management

• Monitor project costs.
• Manage relationships with vendors and contractors when external data tools or consultants are involved.

7. Quality Assurance & Testing Management

• Ensure testing protocols address data validation, transformation logic, and comprehensive pipeline testing.
• Coordinate User Acceptance Testing (UAT) with business stakeholders.

8. Documentation & Reporting

• Maintain comprehensive project documentation, including requirements, data dictionaries, architecture diagrams, and status updates.
• Facilitate knowledge transfer and create handover documentation for ongoing support.

9. Deployment & Change Management

• Oversee implementation and migration strategies, including rollback plans.
• Manage change requests and assess their implications on project scope and timelines.
• Support user training and adoption efforts following the launch.

10. Post-Project Review

• Conduct post-mortem analyses and retrospectives to document lessons learned.
• Evaluate project outcomes against key performance indicators (KPIs) such as data accuracy, system performance, and business impact.

Required Qualifications

• Proficient in Cloud architectures (GCP/AWS), PySpark/Scala data pipelines, metadata management, data warehousing, and scheduling tools.
• Effective communication skills to bridge technical and business teams, along with the ability to make informed trade-off decisions.