Senior Data Engineer

• Designing, building and maintaining production-grade data pipelines and services across AWS, PySpark, Snowflake and SQL-based transformation frameworks
• Developing and maintaining modular, testable and high-quality data models and transformation pipelines using DBT or equivalent structured SQL frameworks
• Improving pipeline robustness through automation, monitoring, validation, dependency management and engineering best practices to reduce manual intervention and production failures
• Diagnosing and resolving pipeline failures, supporting reruns and validations, and addressing data quality issues to maintain continuity of critical customer-facing use cases
• Contributing to CI/CD and DevOps practices including GitHub Actions, version control, testing disciplines and exposure to Infrastructure-as-Code such as Terraform

• Experience with AWS data stack (S3, EMR, EC2, etc.)
• PySpark
• Production-grade data pipelines
• Performance optimisation and large-scale data processing
• Snowflake
• Data modelling (RDV/BDV or similar patterns preferred)
• Query performance tuning
• DBT (or equivalent SQL transformation framework)
• Development of modular, testable data models
• Experience working in structured transformation frameworks (eg dbt, Grid-style approaches)
• CI/CD & DevOps
• Hands-on experience with GitHub Actions (or similar)
• Exposure to Infrastructure-as-Code (eg Terraform)
• Experience working in engineering-led environments with strong SDLC practices
• General engineering maturity
• Strong software engineering principles (testing, modularity, version control)
• Experience working in cross-functional product/data teams
• Ability to contribute to design and solutioning discussions

Desirable Skills:

• Exposure to MLOps/data science pipelines
• Previous experience working in retail/large-scale customer data environments