Senior Data Engineer

The Role

Looking for an experienced Data Engineer to join Data Engineering team for Customer value Understanding and star making value.

Key Responsibilities

The main areas of focus for this role over the next few months will include:

• 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

Your Profile

Essential skills/knowledge/experience:

• 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 (e.g. dbt, Grid-style approaches)

• CI/CD & DevOps

• Hands-on experience with GitHub Actions (or similar)

• Exposure to Infrastructure-as-Code (e.g. 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

⭐ Nice to have

• Exposure to MLOps / data science pipelines

• Previous experience working in retail/large-scale customer data environments