Data Engineer - KT
Summary
Design and build scalable cloud-native data pipelines and lakehouse architectures, integrating AI/ML capabilities for a government data platform using Python, Spark, Kafka, and AWS services.
What Will You Do
. Data Pipeline Infrastructure & Architecture
o Design and implement scalable data architectures on cloud data platforms with high availability, security, and performance
o Lead development of Data Lakehouse solutions
o Collaborate with stakeholders to understand requirements and translate them into technical specifications
. Pipeline Development & Optimisation
o Build and maintain robust ETL/ELT pipelines using modern data engineering tools and frameworks
o Optimise data processing workflows for performance, cost-effectiveness, and reliability
o Implement automated data quality checks and monitoring systems to ensure data integrity
. Data Systems Architecting & Solutioning
o Design and architect comprehensive cloud-native Data & AI solutions aligned with business objectives and technical requirements
o Lead cloud migration strategies and oversee implementation of complex multi-cloud environments
o Drive innovation through integration of Data & AI capabilities into HDB's Data & AI platform product architectures
o Conduct technical assessments and recommend modernised approaches using cloud native technologies
o Maintain architectural documentation
. Cloud Platform Operations
o Leverage Cloud Native Services to build and manage data infrastructure
o Implement infrastructure as code practices using Terraform
o Ensure compliance with security standards and data governance policies
. Technical Leadership & Collaboration
o Mentor junior data engineers and provide technical guidance on complex challenges
o 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 4 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 intelligence platforms
Working Location : Central
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