Software Quality Engineer

Data Pipeline Infrastructure & Architecture Design and implement scalable data architectures on cloud data platforms with high availability, security, and performance Lead development of Data Lakehouse solutions Collaborate with stakeholders to understand requirements and translate them into technical specifications Pipeline Development & Optimisation Build and maintain robust ETL/ELT pipelines using modern data engineering tools and frameworks Optimise data processing workflows for performance, cost-effectiveness, and reliability Implement automated data quality checks and monitoring systems to ensure data integrity Data Systems Architecting & Solutioning Design and architect comprehensive cloud-native Data & AI solutions aligned with business objectives and technical requirements Lead cloud migration strategies and oversee implementation of complex multi-cloud environments Drive innovation through integration of Data & AI capabilities into HDB’s Data & AI platform product architectures Conduct technical assessments and recommend modernised approaches using cloud native technologies Maintain architectural documentation Cloud Platform Operations Leverage Cloud Native Services to build and manage data infrastructure Implement infrastructure as code practices using Terraform Ensure compliance with security standards and data governance policies Technical Leadership & Collaboration Mentor junior data engineers and provide technical guidance on complex challenges Participate in architectural reviews and contribute to data strategy evolution You will be a Great Fit If You Have Bachelor’s degree in computer science, Information Technology, Computer Engineering, or related field Minimum 3 years of relevant experience in data systems architecture, data systems integration, and data pipeline setup at production scale Good understanding of cloud computing principles including infrastructure as code, containerisation, microservices architecture, cloud security frameworks, identity and access management, network architecture, and distributed systems Proven ability to translate business requirements into technical solutions Excellent communication skills for presenting complex concepts to diverse audiences Experience with cloud security frameworks, compliance requirements, and risk management Experience in data domains (e.g. DataOps, Data Lakehouse) and AI/ML Domains (e.g. MLOps, LLMOps) Strong Knowledge and Hands-on experience with SQL, Python and Apache Spark Hands-on experience with Apache Kafka, Airflow, or similar technologies Good to Have: Proficiency in Amazon Web Services (AWS) services Relevant cloud certifications (e.g. AWS Solutions Architect Professional, AWS Data Engineer Associate) would be an advantage Experience with Data & AI cloud-native services (e.g. Amazon SageMaker Unified Studio, Amazon Quick Suite, AWS S3, AWS Glue, AWS Lake Formation, AWS Bedrock, AWS Agent Core). Familiarity with serverless computing, edge computing, and IoT architectures would be an advantage. Experience with machine learning operations (MLOps) and ML model deployment pipelines Knowledge of data governance frameworks and metadata management tools Familiarity with data visualisation tools and business intel