Performance & Strategy: Deliver functionality required for business/data analysts, data scientists, and other roles to advance the overall analytic performance and strategy of the bank.
Infrastructure Strategy: Build best practices and strategies for data infrastructure to fulfill analytic needs using emerging technologies and capabilities.
Data Feed Solutions: Proactively drive efforts to identify data management opportunities and provide solutions for complex data feeds within the bank.
Architecture Evaluation: Evaluate various data architectures in the bank and utilize them to develop data solutions that meet business requirements.
Compliance: Drive the delivery of data products and services into systems and business processes in compliance with internal regulatory requirements.
System Oversight: Oversee the review of internal/external business and product requirements for data operations; suggest changes and upgrades to systems and storage to accommodate ongoing needs.
Data Integration
Strategic Integration: Strategically obtain and integrate data/information from various sources into the firm’s platforms, solutions, and statistical models.
Data Scientist Collaboration: Lead discussions with Data Scientists to understand requirements and create re-usable data assets to enable faster machine learning model deployment.
Pipeline & ETL: Design, build, and maintain optimized data pipelines and ETL solutions as business support tools for analysis and real-time analytics platforms.
Data Quality: Ensure data assets are organized and stored efficiently to guarantee high quality, reliability, flexibility, and efficiency.
Project Management
Operations Management: Manage project conflicts, challenges, and dynamic business requirements to maintain high-performance operations.
Continuous Improvement: Work with team leads to resolve people problems and project roadblocks; conduct post-mortems and root cause analysis to help squads improve productivity.
Talent Development
Mentorship: Mentor and coach junior fellows into fully competent Data Engineers.
Growth: Identify and encourage areas for growth and improvement within the team.
2. KEY RELATIONSHIPS
Direct Manager: Senior Manager / Manager, Data Engineering.
Internal Stakeholders: Teams within the Data Office and relevant departments in the Bank.
External Stakeholders: Partners providing professional services.
3. SUCCESS PROFILE (QUALIFICATIONS & EXPERIENCES)
Qualifications
Bachelor’s or Master’s degree in Statistics, Mathematics, Quantitative Analysis, Computer Science, Software Engineering, or Information Technology.
Work Experience & Technical Skills
Experience:7+ years of relevant experience in developing, debugging, scripting, and employing:
Big Data Tech: Hadoop, Spark, Flink, Kafka, Arrow, Tableau.
Databases: SQL, NoSQL, Graph databases.
Languages: Python, R, Scala, Java, Rust, Kotlin (preference towards functional/trait-oriented).
Language: English proficiency (pursuant to policy).
Architecture & Modeling: Deep experience in designing dimensional data models, ETL processes, data warehouse concepts, and optimized data pipelines. Must have experience as an Architect or worked extensively with one.
System Reliability: Deep experience in monitoring complex systems and solving data/system issues using a consistent, algorithmic approach.
Security: Deep understanding of Information Security principles for compliant handling and management of data.
Agile: Experience working in Agile teams, leading successful digital transformation projects, and mastering Scrum methodologies.
Machine Learning Ops: Proven know-how in scripting and coding to set up, configure, and maintain machine learning model development environments.
Large Scale Delivery: Experience architecting, coding, and delivering high-performance microservices and/or recommenders for (tens of) millions of users.
