Job Description

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

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