Project Data Manager

Key Responsibilities:

Project Planning & Execution

- Lead planning, execution, and delivery of enterprise data projects using Databricks and Confluent.

- Develop detailed project plans, delivery roadmaps, and work breakdown structures.

- Ensure resource allocation, budgeting, and adherence to timelines and quality standards.

Stakeholder & Team Management

- Collaborate with data engineers, architects, business analysts, and platform teams to align on project goals.

- Act as the primary liaison between business units, technology teams, and vendors.

- Facilitate regular updates, steering committee meetings, and issue/risk escalations.

Technical Oversight

- Oversee solution delivery on Databricks (for data processing, ML pipelines, analytics).

- Manage real-time data streaming pipelines via Confluent Kafka.

- Ensure alignment with data governance, security, and regulatory frameworks (e.g., GDPR, CBUAE, BCBS 239).

Risk & Compliance

- Ensure all regulatory reporting data flows are compliant with local and international financial standards.

- Manage controls and audit requirements in collaboration with Compliance and Risk teams.

Required Skills & Experience:

Must-Have:

- 7+ years of experience in Project Management within the banking or financial services sector.

- Proven experience leading data platform projects (especially Databricks and Confluent Kafka).

- Strong understanding of data architecture, data pipelines, and streaming technologies.

- Experience managing cross-functional teams (onshore/offshore).

- Strong command of Agile/Scrum and Waterfall methodologies.

Technical Exposure:

- Databricks (Delta Lake, MLflow, Spark)

- Confluent Kafka (Kafka Connect, kSQL, Schema Registry)

- Azure or AWS Cloud Platforms (preferably Azure)

- Integration tools (Informatica, Data Factory), CI/CD pipelines

- Oracle ERP Implementation experience

Preferred:

- PMP / Prince2 / Scrum Master certification

- Familiarity with regulatory frameworks: BCBS 239, GDPR, CBUAE regulations

- Strong understanding of data governance principles (e.g., DAMA-DMBOK)

Education:

Bachelors or Masters in Computer Science, Information Systems, Engineering, or related field.

KPIs:

- On-time, on-budget delivery of data initiatives

- Uptime and SLAs of data pipelines

- User satisfaction and stakeholder feedback

- Compliance with regulatory milestones