Lead Data Architect

5 months ago

Dubai, United Arab Emirates Emirates NBD Full time

The Lead Machine learning Architect will work very closely with the Advanced Analytics COE Team, Machine Learning use case stakeholders and channel/data architects and is responsible in collaboration with Advanced Analytics COE refine the Analytics architecture to enable fast delivery of reusable, secure and consistent analytics capabilities across the EmiratesNBD Group. This role will engage in strategic initiatives and reports into Head of Data Engineering Chapter and Chief Data Scientist as the Matrix Manager and will document the Machine Learning patterns and related channel integration patterns.

The primary task is to collaborate and help transform the analytics capabilities and enable a data-driven culture across the group and therefore work with Machine Learning Expert, Technology engineers and other architects and platform teams to ensure analytics products are managed as an asset in a standard, and consistent manner in order to maintain consistency, quality observability, using mature technrologies and emerging practices.

This role requires experience in Data science projects/products, MLOps, Feature Store and understanding of big data management and processing related technologies and choices. Has experience in distributed and containerized computing frameworks, infrastructure, automation and techniques.

**Responsibilities**:

- Support design of Machine Learning based use case development with strategic Analytics goals in mind.
- Develop collaboratively analytics architecture standards in conjunction with Machine Learning experts, Data Scientists, business users and other stakeholder.
- Use iterative and incremental approach to achieve long-term objectives
- Work closely with Channel architects to identify/refine integration patterns to deliver analytics output.
- Work closely with Channel architects and platform owners to ensure there is end to end observability.
- Evolve an agile, resilient, and secured Machine Learning architecture landscape.
- Collaboratively help design standards and reference architecture to ensure reusability, automation, low support and a decoupled architecture.
- Create standards for observability, audit, archival and purging. Encourage automation of these tasks.
- Ensure there is both data, operations, and platform level observability.
- Develop data access matrix and ensure right information reaches the right people through secured channels.
- Develop guidelines and solutions to align with internal and external regulatory requirements which is automated.
- Help refine methods and procedures for tracking feature quality, completeness, redundancy, compliance, and observability.
- Identify and document integration points to data management tools and define required protocols for integration.
- Work with Data science community to define the guidelines/patterns for capturing feature lineage and traceability.
- Collaborate with Data science/Data Engineering communities to define feature quality guidelines.
- Support Chief Data Scientist in definition of analytics governance and maturity roadmap.
- Ensure standards, process and observability is in place to guarantee data used by AACOE is timely and fit for purpose.
- In collaboration with Data/Platform architect ensure there is data and platform level observability.
- Work closely with Data architects to ensure the data consumed by the analytics team is timely, available, observable, and fit for purpose.
- Ensure there are exception handling processes in upstream platforms.
- Ensure there are documented standards for upstream pipeline scheduling and observability.

**Requirements**:

- Master or bachelor’s degree in computer science, information systems management or related field.
- Solution and Architecture certifications such as TOGAF or other.
- More than 8 years of experience in information technology where recent experience in Machine Learning architecture, data architecture or technology solutions definitions and implementations.
- More than 6 years of experience in leadership, technology road-mapping and exposed to software development.
- Extensive experience in banking and financial services domain.
Production experience and expertise in Machine Learning architecture, Feature store and MLOps.
- Machine Learning Architecture & Data architecture for large and complex organization and systems and implemented large scale end-to-end Analytics & Data solutions.
- Experience in leveraging traditional Data Warehousing/Lake to build out data science pipelines.
- Experience and expertise in designing and building/using Feature store (home grown, Feast, SageMaker etc.), MLOps (home grown). observability and high level of automation.
- Experience in container-based pipelines and deployments like Kubernetes, OpenShift, AKS, EKS.
- Extensive experience working with Continuous Integration (CI) and Continuous Development (CD) environments using tools like GitHub Actions, Azure DevOps, Jenkins.
- Expertise with normalized OLTP,

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