At Fenergo, our aim is to future-proof Client Lifecyle Management through our award-winning SaaS based solutions that empower financial institutions. With the expertise of our 700 employees across 11 offices worldwide we enable our clients to improve operational efficiencies and return on investment, manage compliance with KYC, AML and reduce risk with continuous monitoring of clients and transactions throughout the client lifecycle.
Fenergo dynamically manages any client type, any product type and every client journey for all segments, channels and devices.
As a company we value collaboration with cross functional teams, driving employee engagement and are champions of diversity and inclusion. As we continue to grow and evolve, we look for team members who will make a meaningful impact, contribute to a positive work environment and shape the future of our organisation.
Join us as we build a workplace where talent thrives and together, we achieve new heights of success!

WHAT WILL YOU DO?

As a Senior Data Platform Architect, you will lead the design, implementation, and operation of a data lake using AWS data lake technologies. You will with work closely with a ringfenced team of system engineers, data analysts and a delivery manager to develop robust, scalable, and secure data infrastructure that aligns with our business objectives and analytics needs. This role requires deep expertise in AWS cloud technologies, hands-on implementation skills, and the ability to guide teams in deploying advanced data solutions.

RESPONSIBILITIES:

• Design, Build, and Operate Data Lakes - Architect, develop, and manage scalable data lake solutions using AWS services such as Amazon S3, AWS Glue, AWS Lake Formation, and Amazon Athena.
• Data Ingestion Pipelines - Design and implement data ingestion pipelines using AWS Glue, Amazon Kinesis, and other relevant services to ensure efficient, real-time data collection.
• Data Processing and Transformation - Utilise AWS EMR, Apache Spark, and AWS Lambda for ETL processes and large-scale data processing.
• Data Cataloguing and Management - Implement and manage metadata using AWS Glue Data Catalog to enhance data discoverability and governance.
• Collaboration with Teams - Partner with development, data engineering, and analytics teams to integrate applications and analytics platforms with the data lake.
• Performance Optimisation - Continuously improve the performance and cost efficiency of the data lake architecture.
• Security and Compliance - Implement robust data security measures using AWS IAM, AWS KMS, and AWS Lake Formation to maintain compliance with data regulations and best practices.
• Technical Leadership - Provide mentorship and technical direction to team members, ensuring best practices are followed in data architecture and cloud deployments.
• Stay Current - Maintain expertise in emerging AWS data technologies and trends, applying them were beneficial to business objectives.