Data Engineer (AWS Lakehouse) (1 Year Contract)

About the Role

Rhino Partners is looking for an experienced Data Engineer to design, build, and deploy a scalable and reliable data lakehouse platform on AWS.

The successful candidate will be responsible for developing end-to-end data pipelines, implementing data quality and governance frameworks, and delivering production-ready data solutions. This role requires strong hands-on AWS expertise, experience with modern lakehouse architectures, and the ability to build robust, cost-efficient data infrastructure.

Key Responsibilities

• Design, develop, and maintain scalable ETL/ELT pipelines using AWS services, including AWS Glue, Step Functions, Lambda, and S3.
• Architect and implement data lakehouse solutions using Apache Iceberg or Amazon S3 Tables, incorporating schema evolution, partition evolution, and ACID transactions.
• Optimise data pipelines and storage for performance, scalability, reliability, and cost efficiency.
• Define and implement automated data quality validation frameworks to ensure data accuracy, completeness, and consistency.
• Establish data quality metrics, monitoring, and alerting mechanisms to identify and resolve data issues.
• Implement and maintain data governance standards and ensure compliance with organisational requirements.
• Develop production-quality code and deploy data engineering solutions on AWS cloud infrastructure.
• Implement CI/CD pipelines and infrastructure-as-code using Terraform or AWS CloudFormation to support repeatable and auditable deployments.
• Collaborate with cross-functional teams to translate business and technical requirements into effective data solutions.
• Produce comprehensive technical documentation and facilitate knowledge transfer and handover to Day 2 operations teams.

Requirements

• Bachelor's degree in Computer Science, Data Engineering, Information Systems, or a related discipline.
• Minimum 3–5 years of hands-on experience in data engineering, ETL/ELT development, or data platform engineering.
• Strong experience with AWS data engineering services, particularly AWS Glue, Step Functions, Lambda, and S3.
• Hands-on experience designing and implementing data lakehouse architectures using Apache Iceberg or similar open table formats.
• Strong understanding of Apache Iceberg capabilities, including schema evolution, partition evolution, ACID transactions, and table optimisation.
• Experience implementing automated data quality checks, validation frameworks, and data governance practices.
• Proficiency in writing production-quality code and optimising data pipelines for performance and reliability.
• Experience with CI/CD practices and infrastructure-as-code tools such as Terraform or AWS CloudFormation.
• Strong problem-solving skills and the ability to communicate technical concepts effectively to both technical and non-technical stakeholders.
• Ability to work independently and collaboratively in a cross-functional environment.

Good to Have

• Experience working within a Government Commercial Cloud (GCC) environment.
• AWS certifications, such as AWS Certified Data Analytics or AWS Certified Solutions Architect.
• Experience supporting production data platforms, including operational handover and ongoing maintenance.