Data Engineer - KT
Summary
Design and build scalable cloud data pipelines and lakehouse architectures using Python, Spark, Kafka, and Airflow to power AI/ML workloads for a global fashion brand.
What Will You Do?
- 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 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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