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 Sage Maker 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 visualization tools and business intelligence platforms