Key Responsibilities
- Project Planning & amp, Execution
- Lead planning, execution, and delivery of enterprise data and MI automation projects using Databricks and Confluent.
- Develop detailed requirements, project plans, delivery roadmaps, and work breakdown structures.
- Ensure resource allocation, budgeting, and adherence to timelines and quality standards.
- Manage vendors deliverables and quality of output.
- Manage issues, conflicts and prepare mitigation
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).
Must-Have
Required Skills & Experience:
- 7+ years of experience in Project Management within the banking or financial services
sector.
- Proven experience leading data and MI automation 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
- PowerBI
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) & Bachelor's or Master's 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
