AI Data Engineer
Summary
Hands-on AI Data Engineer designing and optimising scalable ETL/ELT data pipelines and infrastructure to power AI/ML initiatives, using Python, SQL, Airflow/dbt, and major cloud platforms (Azure/AWS/GCP).
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.
Requirements
- 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.
Ideal Candidate
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/lakehouses, streaming, vector databases, RAG pipelines or AI data platforms would be highly advantageous.
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