Manager, Data Engineering

Job Purpose

• Lead a data engineering team to design and develop programs, algorithms, and automated processes to cleanse, integrate, and evaluate large-scale datasets from multiple sources.
• Implement complex business logic using available data processing tools.
• Integrate new data sources to increase system throughput and manage data pipelines supporting robust analytics.
• Source and prepare data to ensure completeness and quality on metadata platforms.

Key Accountabilities

1. Data Architecture

• Deliver data functionality to support business analysts, data analysts, data scientists, and other stakeholders in advancing the bank’s analytics strategy.
• Establish best practices and strategies for data infrastructure using emerging technologies to meet analytics and data utilization needs.
• Guide the team in identifying data management opportunities and providing solutions for complex data feeds.
• Evaluate and leverage various data architectures within the bank to meet business requirements.
• Drive delivery of data products and services in compliance with internal regulatory requirements.
• Review internal and external business/product requirements and propose system or storage upgrades to support evolving data needs.

2. Data Integration

• Strategically source and integrate data from multiple systems into enterprise platforms, solutions, and statistical models.
• Collaborate with Data Scientists to understand data requirements and create reusable data assets to accelerate ML model development and deployment.
• Design, build, and maintain optimized data pipelines and ETL solutions to support analytics and real-time decision-making.
• Ensure data assets are well-organized, high quality, reliable, flexible, and efficient.

3. Project Management

• Manage project conflicts, challenges, and changing business requirements to maintain high operational performance.
• Work with team leads to resolve people issues and project roadblocks.
• Conduct post-mortems and root cause analysis to continuously improve delivery practices and productivity.

4. People Management

• Attract, onboard, and retain high-performing talents.
• Communicate team and individual KRAs/KPIs, goals, action plans, expectations, and results.
• Manage performance and provide regular feedback in line with the annual performance management cycle.
• Support professional and personal development through coaching, training, capability assessment, and feedback.
• Motivate and recognize team members’ contributions.
• Develop internal talents and succession within the team.
• Act as a role model and promote corporate culture at sub-function level.
• Understand and communicate relevant HR policies and offerings to team members.

Key Relationships

Direct Manager

• Head of Data Engineering and Delivery

Direct Reports

• Data Engineer
• Senior Data Engineer

Internal Stakeholders

• Data Office teams
• Business Tribes
• Enabling Tribes (IT, Data Engineering, Data Governance)
• Team Leads and Business Product Owners across Business, Finance, Risk, Corporate Affairs, and IT

External Stakeholders

• Partners and vendors providing professional services

Success Profile

Qualifications

• Bachelor’s or Master’s degree in Statistics, Mathematics, Quantitative Analysis, Computer Science, Software Engineering, or Information Technology.

Experience

• Minimum 4+ years of experience in big data technologies (e.g. Hadoop, Spark, Flink, Kafka, Arrow, Tableau), databases (SQL, NoSQL, Graph), and programming languages (Python, R, Scala, Java, Rust, Kotlin).
• At least 1+ year in an equivalent managerial role.
• Strong experience in dimensional data modeling, ETL processes, data warehouse methodologies, and optimized data pipelines.
• Prior experience acting as a data architect or working closely with one.
• Proven ability to monitor complex systems and resolve data/system issues using a structured, algorithmic approach.
• Solid understanding of information security principles and compliant data handling.
• Proven track record working in Agile environments and leading digital transformation initiatives; strong knowledge of Agile and Scrum methodologies.
• Strong knowledge of Data & Analytics trends and emerging technologies.
• Experience designing and delivering high-performance microservices and/or recommendation systems serving millions of users.

Functional Competencies

• Computation Modeling – Level 3
• Data Governance – Level 3
• Data Management – Level 3
• Data Mining – Level 3
• Metadata Management – Level 3
• Programming – Level 3

Leadership Competencies

• Change and Innovation – Level 2
• Collaboration – Level 2
• Results Orientation – Level 2
• Strategic Mindset – Level 2
• Talent Development – Level 2

Culture Fit (Core Values)

• Innovation and Creativeness
• Collaboration for Common Objectives
• Self-development
• Customer Centricity

Personal Attributes

• Integrity and modesty
• Optimistic, adaptable, emotionally controlled
• Conscientious, resilient, energetic, achievement-oriented
• Persuasive, unconventional, forward-thinking