About the Role
My client is looking for a hands‑on AI Data Engineer to design build and optimise scalable data infrastructure and pipelines that power advanced AI and analytics initiatives You will work closely with Software Engineering and AI ML teams to ensure high-quality reliable and accessible data across structured and unstructured datasets
Key Responsibilities- Design develop and maintain robust ETL ELT data pipelines across multiple data sources
- Build and optimise scalable data workflows using technologies such as Airflow dbt or equivalent orchestration tools
- Process and transform complex structured and unstructured datasets
- Develop data solutions that support AI ML and advanced analytics use cases
- Implement data validation testing monitoring and quality frameworks
- Diagnose data issues and proactively improve pipeline reliability and performance
- Collaborate with AI ML and Software Engineers to provide reliable and accessible data
- Apply best practices around data governance security quality and lineage
- Document data architectures metadata and data flows to ensure transparency and reproducibility
- Optimise data pipelines and infrastructure for scalability performance and cost efficiency
- 5 years of experience in Data Engineering ETL development or Data Warehousing
- Strong hands‑on experience with Python and SQL
- Experience with Airflow dbt Luigi or similar workflow orchestration technologies
- Strong understanding of big data architectures and scalable data pipelines
- Experience working with large-scale structured and unstructured datasets
- Hands‑on experience with at least one major cloud platform Azure AWS or GCP
- Strong understanding of data quality validation monitoring and governance
- Experience diagnosing complex data and pipeline issues
- Strong communication skills with the ability to work with both technical and non‑technical stakeholders
- Bachelor s degree in Computer Science Engineering or a related technical discipline
- 5+ years of experience in Data Engineering, ETL development or Data Warehousing.
- Strong hands‑on experience with Python and SQL.
- Experience with Airflow, dbt, Luigi or similar workflow orchestration technologies.
- Strong understanding of big data architectures and scalable data pipelines.
- Experience working with large-scale structured and unstructured datasets.
- Hands‑on experience with at least one major cloud platform:
Azure, AWS or GCP. - Strong understanding of data quality, validation, monitoring and governance.
- Experience diagnosing complex data and pipeline issues.
- Strong communication skills with the ability to work with both technical and non‑technical stakeholders.
- Bachelor s degree in Computer Science, Engineering or a related technical discipline.
The ideal candidate will be a production‑focused Data Engineer rather than a BI or analytics specialist with a strong engineering mindset and experience building data platforms that support modern AI ML environments Experience with Spark data lakes lake houses streaming vector databases RAG pipelines or AI data platforms would be highly advantageous
The ideal candidate will be a production‑focused Data Engineer rather than a BI or analytics specialist, with a strong engineering mindset and experience building data platforms that support modern AI/ML environments . Experience with Spark, data lakes/lake houses, streaming, vector databases, RAG pipelines or AI data platforms would be highly advantageous
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