PROJECT MANAGER - BANKING - DATA & AI

Job Purpose

The Project Manager will lead the end-to-end delivery of a large-scale Data Lake Modernization program for a banking client, encompassing both the technical migration/modernization of the data platform and the implementation of downstream AI use cases — specifically hyper-personalization of banking products and Next Best Offer (NBO) capabilities. 

The role requires strong command of both traditional data/infrastructure delivery and AI/ML-enabled business transformation, bridging technical teams, business stakeholders, and senior banking client sponsors.

Key Responsibilities

• Own end-to-end project planning, governance, and delivery of the data lake modernization program, including scope, timeline, budget, resourcing, and risk/issue management.
• Lead delivery of the AI/ML use case workstream for hyper-personalization and Next Best Offer, coordinating data scientists, ML engineers, and banking product/marketing stakeholders.
• Define and manage the project roadmap covering data platform migration (legacy to modern data lake/lakehouse architecture), data pipeline modernization, data governance, and AI model deployment.
• Act as the primary point of contact for senior client stakeholders (CIO/CDO/Head of Marketing/Retail Banking) — managing expectations, steering committees, and executive reporting.
• Coordinate cross-functional teams including Data Engineering, Data Science/AI, Cloud/Infrastructure, Business Analysts, and Change Management resources.
• Ensure alignment between the data lake modernization workstream and AI use case delivery, sequencing dependencies correctly (e.g., data readiness before model deployment).
• Manage third-party vendors/technology partners (e.g., cloud providers, data platform vendors) as required for the project.
• Drive adoption of Agile/hybrid delivery methodologies, managing sprints, backlogs, and release planning for both data engineering and AI workstreams.
• Ensure robust data governance, data quality, and regulatory compliance (SAMA, data privacy/PDPL) are embedded throughout the modernization effort.
• Track and report on business value realization from AI use cases (e.g., uplift in offer acceptance rates, personalization engagement metrics, campaign ROI).
• Identify, escalate, and mitigate project risks, particularly around data migration integrity, model performance, and change adoption.
• Support change management and business readiness activities to ensure banking product and marketing teams can effectively operationalize NBO/hyper-personalization outputs.
• Prepare and present steering committee packs, status reports, and business case tracking for senior leadership and client sponsors.

Required Qualifications

• Bachelor's degree in Computer Science, Data/Information Systems, Engineering, Business, or a related field. Master's degree (MBA or technical) an advantage.
• Project management certification such as PMP, PRINCE2, or equivalent required.
• Agile certification (e.g., CSM, SAFe) highly desirable given hybrid delivery approach.

Experience

• Minimum 7+ years of project/program management experience, including at least 3+ years managing large-scale data platform/data lake or data engineering programs.
• Prior experience delivering AI/ML-enabled use cases in a banking or financial services context — experience with personalization engines, recommendation systems, or Next Best Offer/Next Best Action is strongly preferred.
• Experience working within a consulting environment, managing client relationships, steering committees, and multi-vendor delivery teams.
• Experience in the banking/financial services sector in Saudi Arabia or the broader GCC strongly preferred; familiarity with SAMA regulatory and data governance requirements is a plus.
• Proven track record managing cross-functional teams spanning data engineering, cloud infrastructure, data science, and business/marketing stakeholders.
• Experience with cloud data platforms (e.g., AWS, Azure, GCP, Snowflake, Databricks) and modern data architecture concepts (data lakehouse, data mesh) is highly valued.

Skills & Competencies

• Strong understanding of data lake/lakehouse architecture, ETL/ELT pipelines, and data migration methodologies.
• Working knowledge of AI/ML concepts relevant to personalization and recommendation systems (e.g., propensity modeling, customer segmentation, real-time decisioning).
• Excellent stakeholder management and executive communication skills — able to translate technical complexity into business value for senior banking executives.
• Strong Agile/hybrid project delivery skills, including backlog management, sprint planning, and cross-team dependency management.
• Financial and commercial acumen — budget management, business case development, and value tracking.
• Strong risk and issue management capability, particularly in complex, multi-workstream technical programs.
• Excellent skills with PM tools (JIRA, MS Project, Confluence) and reporting/presentation tools (PowerPoint, Excel).
• Fluency in English required; Arabic language proficiency strongly preferred given local client stakeholder engagement.
• Comfortable operating in a fast-paced consulting environment with high client visibility and multiple concurrent priorities.

Key Performance Indicators (Typical)

• On-time, on-budget delivery of data lake modernization milestones.
• Successful deployment and adoption of AI use cases (hyper-personalization, NBO) into production.
• Measurable uplift in personalization/offer performance metrics post-deployment (e.g., acceptance rate, engagement, cross-sell).
• Client satisfaction and steering committee feedback scores.
• Effective risk mitigation — minimal critical delivery delays or escalations.

Working Relationships

• Internal: Data Engineering, Data Science/AI teams, Cloud/Infrastructure Architects, Business Analysts, Change Management, Practice/Engagement Leadership.
• External: Client CIO/CDO/Head of Retail Banking & Marketing, client IT and business teams, technology/cloud vendors, third-party AI/data platform providers.

Vertical:

Technology