Senior Data Engineer

Description :

Role & Responsibilities :

Data Platform Engineering :

- Design and build scalable data pipelines and services on AWS.

- Develop and maintain Python-based data processing frameworks.

- Build ingestion pipelines integrating multiple data sources including APIs and customer data feeds.

- Develop and maintain transformation workflows using DBT.

Data Modelling and Architecture :

- Design and implement relational data models supporting analytics and operational workloads.

- Build and maintain performant schemas within AWS RDS MySQL.

- Contribute to the design of scalable, performant data models and transformation layers.

Data Processing and Transformation :

- Implement scalable transformation pipelines including data standardisation and deduplication.

Performance and Reliability :

- Optimise SQL queries and ETL workloads for performance.

- Implement data validation and quality assurance processes.

- Build observable pipelines with logging and monitoring.

- Ensure pipelines are resilient, maintainable, and scalable.

Preferred candidate profile :

Core Technical Skills :

- Strong Python development experience in data engineering environments.

- Extensive experience with AWS RDS MySQL.

- Experience using DBT for data transformation and modelling.

- Deep understanding of relational database design.

Data Engineering :

- Experience designing and building large-scale ETL/ELT pipelines.

- Experience working with high-volume datasets.

- Strong understanding of scalable data pipeline architecture.

AWS Experience :

- AWS services including AWS RDS MySQL, S3, DMS, Lambda, and Cloudwatch.

Engineering Practices :

- Experience working with Git-based development workflows.

- Ability to design maintainable and scalable engineering solutions.

Desirable :

- Experience supporting or enabling machine learning workflows.

- Experience building data deduplication or identity resolution systems.

- Experience working in multi-tenant data platforms.

Behaviours and Attributes :

- Strong systems-thinking mindset.

- Structured and reliable approach to managing data workflows.

- Proactive and curious about improving data processes through automation.

- Communicates clearly with both technical and non-technical audiences.

- Takes ownership of data quality and operational outcomes.

- Collaborative and solutions oriented.

- Committed to continuous improvement and process scalability.