Technology is our how. And people are our why. For over two decades, we have been harnessing technology to drive meaningful change.
By combining world-class engineering, industry expertise and a people-centric mindset, we consult and partner with leading brands from various industries to create dynamic platforms and intelligent digital experiences that drive innovation and transform businesses.
From prototype to real-world impact - be part of a global shift by doing work that matters.
- Responsible for ingestion of data sources and feature engineering to support creation of modelling data layers, integrate data into models / workflows.
- Solution design, implement, and maintain data pipelines for data ingestion, processing, and transformation using could services and other technologies
- Create and maintain data solutions IN Azure, using any of the following: Data Factory, Synapse, Fabric
- Implementing data validation and cleansing procedures will ensure the quality, integrity, and dependability of the data.
- Improve the scalability, efficiency, and cost-effectiveness of data pipelines.
- Monitoring and resolving data pipeline problems, ensuring consistency and availability of the data.
Qualifications
- Good Python skills
- Knowledge of good data design principles (concepts such as Kimball and Star Schema), data warehousing concepts and data modelling.
- Strong experience with Relational and ideally NoSQL databases.
- Strong understanding of ETL/ELT processes.
- Exposure to data monitoring and observability tools
At Endava, we’re committed to creating an open, inclusive, and respectful environment where everyone feels safe, valued, and empowered to be their best. We welcome applications from people of all backgrounds, experiences, and perspectives—because we know that inclusive teams help us deliver smarter, more innovative solutions for our customers. Hiring decisions are based on merit, skills, qualifications, and potential. If you need adjustments or support during the recruitment process, please let us know.
