Deliver audit-ready and traceable solutions to support assurance, compliance reviews, and fraud risk monitoring.
Develop and deploy data models that align with diverse business requirements across compliance, audit, fraud detection, and assurance.
Design, develop, and maintain data pipelines for collecting, transforming, and loading data into various data stores.
Build and maintain data warehousing and data lake solutions
Develop and deploy data models that support various business requirements
Write efficient and scalable code in languages such as Python, Scala, or Java
Lead the design of data solutions with quality, automation, and performance in mind
Own the data pipelines feeding into the Data Platform ensuring they are reliable and scalable
Ensure data is available in a fit-for-purpose and timely manner for business and analytics consumption
Work with Data Governance team to ensure solutions are compliant with regulations such as GDPR and CISO policies and data quality is baked-in to pipelines
Collaborate with cross-functional teams to understand data requirements and provide support for data-driven initiatives
Set and embed standards for systems and solutions, and share knowledge to keep the team engaged and skilled in the latest technology
Prototype and adopt new approaches, driving innovation into the solutions
Support alignment of requirements and sources of data from line of business systems and other endpoints
Mentor and guide junior members of the team to help them get up to speed in a short amount of time
Communicate complex solutions in a clear and understandable way to both experts and non-experts.
Stay up-to-date with industry trends and technology advancements in data engineering.
Requirements
Extensive experience leading AWS and cloud data platform transformations
Proven track record of delivering large-scale data and analytical solutions in a cloud environment
Hands-on experience with end-to-end data pipeline implementation on AWS, including data preparation, extraction, transformation & loading, normalization, aggregation, warehousing, data lakes, and data governance
Expertise in developing Data Warehouses
In-depth understanding of modern data architecture such as Data Lake, Data Warehouse, Lakehouse, and Data Mesh
Strong knowledge of data architecture and data modelling practices
Familiarity with CI/CD driven data pipeline and infrastructure
Agile delivery approach using Scrum and Kanban methodologies
Innovative problem-solving skills and ability to provide clear recommendations
Experience managing and leading a small team of data engineers
Strong knowledge of how data drives analytical and reporting activities, including automated marketing and personalization capabilities
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