Data Engineer

Role Summary

We are seeking an experienced Data Engineer to design, build, and optimise modern data pipelines and transformation workflows on cloud-based platforms. The role will focus on ingesting raw source data, structuring it into curated layers, and ensuring it is reliable, governed, and optimised for analytics and reporting.

Key Responsibilities

• Design, develop, and maintain data ingestion pipelines using cloud ETL services (e.g., AWS Glue, DMS, Azure Data Factory, or equivalent).


• Transform and integrate source system data into structured formats across landing, curated, and reporting layers.


• Collaborate with Data Architects to implement canonical data models and conformed dimensions.


• Rebuild or migrate existing transformation logic from legacy BI/ETL tools into modern data pipelines.


• Support migration of historical datasets into cloud storage and analytics layers.


• Implement logging, monitoring, and exception handling to ensure reliability and auditability of pipelines.


• Work with BI and application engineers to ensure dashboards and workflows operate effectively on the curated data layer.


• Participate in data validation and reconciliation exercises against legacy outputs.


• Contribute to UAT cycles and provide timely fixes for defects.


• Document pipelines, data lineage, and transformation logic for long-term maintainability.

Required Skills & Experience

• 8+ years of professional experience as a Data Engineer.


• Strong hands-on experience with cloud data services (e.g., AWS Glue, S3, Redshift, DMS, or Azure equivalents).


• Proficiency in SQL development and handling unstructured or poorly keyed source data.


• Experience with data modelling techniques (3NF, star schema, dimensional modelling).


• Familiarity with reverse engineering transformations from legacy ETL/BI systems.


• Strong knowledge of data validation, reconciliation, and quality assurance techniques.


• Experience in optimising ETL/ELT pipelines for performance and scalability.


• Practical experience with DevOps practices CI/CD pipelines, version control (Git), automated testing, and deployment of data workflows.


• Effective problem-solving and troubleshooting skills in distributed data environments.


• Strong communication and teamwork skills, with experience collaborating with architects, BI developers, and business SMEs.

Desirable Skills

• Exposure to BI platforms (e.g., Power BI, Tableau, Qlik) and their integration with cloud data sources.


• Knowledge of write-back or data entry workflows integrated into analytics platforms.


• Experience using AI-assisted coding tools (e.g., GitHub Copilot, ChatGPT, or similar) to accelerate development, improve data pipeline efficiency, or automate testing/documentation.


• Exposure to AI/ML-driven data engineering workflows (e.g., anomaly detection in pipelines, automated data quality monitoring).


• Domain knowledge in manufacturing, supply chain, or financial reporting is advantageous.