Data Engineer (AWS Lakehouse) (1 Year Contract)
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Summary
A 1-year contract data engineer role designing, building, and deploying a scalable data lakehouse platform on AWS. Day to day, the person develops ETL/ELT pipelines with AWS Glue, Step Functions, Lambda and S3, implements Apache Iceberg table formats, builds data quality/governance frameworks, and ships infrastructure as code with Terraform or CloudFormation.
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.
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.