Data Engineer

Responsibilities and Requirements Design and implement scalable ETL/ELT pipelines using AWS services including Glue, Step Functions, Lambda, and S3 Architect and build data lakehouse solutions leveraging Apache Iceberg or S3 Tables Implement lakehouse capabilities including schema evolution, partition evolution, and ACID transactions Optimise data pipelines for performance, cost efficiency, scalability, and reliability Design and implement data processing workflows supporting production-grade data platforms Define, implement, and maintain automated data quality validation frameworks Establish data quality metrics and monitoring to ensure accuracy, completeness, and consistency of data Enforce data governance standards and ensure compliance across the data platform Write production-quality code for data engineering solutions Deploy solutions on AWS cloud infrastructure using modern DevOps practices Develop and maintain CI/CD pipelines and infrastructure-as-code practices using Terraform, CloudFormation, or equivalent tools Ensure deployments are repeatable, auditable, and aligned with operational requirements Collaborate with cross-functional teams and communicate technical designs clearly to non-technical stakeholders Produce technical documentation to support handover of developed solutions to Day 2 operations teams Design and build scalable and reliable data pipelines using modern data lakehouse architectures Apply strong understanding of Apache Iceberg features and S3 table optimisation techniques Implement automated validation frameworks and governance controls for enterprise data platforms Optimise application performance and cloud deployments Support production-ready data engineering solutions in AWS environments Degree in Computer Science, Data Engineering, Information Systems, or related field At least 3–5 years of experience in data engineering, ETL/ELT development, or data platform roles Strong hands-on experience with AWS services Strong proficiency in Ap