Director

Role Overview :

The Director of Data Engineering & AI Platforms will lead the design, development, and delivery of scalable data engineering platforms that power analytics, AI/ML, and intelligent decisioning across the bank. This leader will own the end-to-end data engineering strategy-spanning modern data platforms, cloud data architecture, and AI-ready data pipelines-while providing hands-on technical leadership to teams of data engineers and platform engineers.

This role sits at the intersection of data engineering, database architecture, and applied AI, ensuring data products are reliable, scalable, secure, and optimized for advanced analytics and AI use cases across consumer and commercial banking.

Key Responsibilities :

Leadership & Strategy :

- Lead, inspire, and develop high-performing data engineering teams, fostering a culture of engineering excellence, ownership, and continuous improvement.

- Define and execute the data engineering strategy supporting analytics, AI/ML, GenAI, and enterprise reporting use cases.

- Establish architectural standards for relational and non-relational databases, data lakes, and AI-ready data platforms.

- Partner closely with Data Science, Product, Enterprise Architecture, Risk, and Security to ensure data solutions align with business priorities, regulatory requirements, and responsible AI standards.

- Communicate strategy and delivery plans clearly across global teams, leading with empathy and treating team members as people, not resources.

- Drive a people-first, non-hierarchical culture - fostering open communication across all levels, internal mobility, learning, and development.

Data Engineering & Architecture :

- Serve as a hands-on technical leader in the design of scalable data pipelines, data stores, and information flows across the enterprise.

- Design and optimize cloud-based big data platforms, including ingestion, transformation, storage, and consumption layers.

- Lead the engineering of ETL/ELT frameworks, streaming pipelines, and batch processing solutions.

- Conduct enterprise-wide assessments of data stores and data flows to identify bottlenecks, friction points, and modernization opportunities.

- Own data modeling standards to ensure alignment with business objectives, performance, and accessibility.

AI Enablement & Advanced Analytics :

- Enable and support AI/ML and GenAI initiatives by building reliable, high-quality, and well-governed data pipelines.

- Collaborate with Data Science teams to operationalize models, including feature engineering pipelines, inference data flows, and model monitoring data.

- Support AI-driven use cases such as predictive analytics, recommendations, NLP-based insights, and intelligent automation.

- Stay current with market trends, embed innovative practices into strategy, and drive the organization forward with an AI-first approach - ensuring AI initiatives move beyond proof-of-concept to enterprise-scale solutions.

- Approach data engineering with an AI mindset and vice versa, reflecting the evolving and inseparable nature of the two disciplines.

Delivery, Reliability & Governance :

- Ensure teams deliver high-quality solutions with clear requirements, strong engineering discipline, and predictable delivery.

- Implement best practices across CI/CD, DevOps, data quality checks, monitoring, and observability.

- Embed data governance, security, privacy, and compliance controls across all data platforms.

- Ensure platforms meet enterprise standards for availability, scalability, and resiliency.

- Lead an enabling team responsible for building foundational platforms, tools, and guardrails, supporting multiple arms of AI engineering and enabling the broader organization.

- Lead multiple scrum pods or functional teams, with accountability for recruitment, upskilling, and technical leadership across the enablement structure.

Enterprise Competencies :

Learning Agility :

- Stays current with rapidly evolving AI, data engineering, and cloud technologies; continuously embeds new knowledge into platform strategy and team practices.

- Understands and bridges both data and AI engineering disciplines, adapting quickly as these fields converge.

Customer Centricity :

- Ensures data platforms and pipelines are designed around the needs of internal teams, end users, and the business - delivering reliable, governed, and accessible data products.

- Communicates strategy and technical direction with empathy and clarity across all levels, from engineers to executives.

Tenacity / Persistence :

- Balances empathy with a strong delivery focus - drives teams to meet high standards with predictable outcomes even in complex, large-scale environments.

- Removes impediments, resolves conflicts constructively, and maintains momentum across multiple teams and workstreams without losing sight of the long-term platform vision.

Required Qualifications :

- 12+ years of experience in data engineering, database engineering, or platform engineering, including 5+ years in senior technical leadership roles.

- Proven experience leading teams building large-scale data platforms in cloud environments.

- Deep hands-on expertise with :

1. Big data ecosystems (Hadoop, Spark, Hive, HDFS, etc.)

2. ETL/ELT tools and frameworks (Informatica, DataStage, custom frameworks, etc.)

3. Relational & non-relational databases (Teradata, Oracle, SQL Server, DB2, Redshift, NoSQL).

4. Programming & data technologies : SQL, Python, Spark, Scala, Java, shell scripting.

- Strong experience with AWS data services (S3, Glue, Athena, RDS, Redshift, etc.).

- Solid understanding of distributed systems, data architecture, and performance optimization.

- Demonstrated ability to partner with senior stakeholders and influence across technology and business teams.

- Hands-on experience with AI technologies; ability to understand and implement new advancements and articulate technical details to both engineering teams and executives.

- Financial discipline - ability to manage budget and financial responsibilities at a team and platform level.

Desired Qualifications :

- Experience in financial services, with understanding of consumer and commercial banking data.

- Experience supporting or enabling AI/ML and GenAI solutions, including feature pipelines and analytics platforms.

- Familiarity with data visualization and BI tools (Tableau, Cognos, SAS).

- Knowledge of responsible AI, data governance, and regulatory considerations in highly regulated environments.

- Experience modernizing legacy data platforms into cloud-native architectures.

- Executive speaking skills - ability to articulate strategy, challenge the status quo, and present to senior leadership and key stakeholders with confidence.

- Experience working across or within highly collaborative, non-hierarchical organizational cultures with an emphasis on peer relationships and open communication.

Education & Certifications :

- Required : Bachelor's degree in Computer Science, Engineering, Statistics, or related field.

- Preferred : Master's degree in Computer Science, Data Engineering, AI/ML, or related discipline.

- Preferred : AWS, Big Data, or Agile certifications.