The role requires understanding and translating business needs into data models, creating robust data pipelines, and developing and maintaining data lakes.

It also involves implementing and maintaining CI/CD pipelines for data solutions. The ideal candidate will be proficient in big data tools preferably on Azure Stack like ADF, Databricks, Synapse Analytics, Azure DevOps, programming languages such as Spark (Scala/Python), SQL, and have strong analytical skills related to working with structured and unstructured datasets.

This role is for Data Engineering team that works on data needs for the whole enterprise. The group focuses on collection, organisation, governance, and access management of data to support productivity, efficiency, and decision-making for the business.

• Design, develop, implement, test, and operate large-scale, high-volume, high-performance data structures for analytics and Reporting.
• Use best engineering practices and build frameworks where necessary, for data ingestions, curation and consumption patterns
• Implement data structures using best practices in data modelling, ETL processes, and SQL and OLAP technologies, Model data and metadata for ad hoc and pre-built reporting.
• Ensure data delivered is of the highest quality
• Work with product tech teams and build robust and scalable data integration (ETL) pipelines using SQL, Python and Spark.
• Will be responsible to adhere to best coding and data practices.

Communicate effectively with product/business/tech-teams/other Data teams

• Bachelor's degree/Master’s degree in Engineering, Computer Science, or related field (or equivalent work experience in a related field)
• 2-4 years of experience in Data Engineering, data modelling, Data Warehousing and ETL pipelines
• Good proficiency in SQL, Scala/Python (PySpark), Scripting (Shell/Python), CI/CD, Git
• Has worked on data stack on Azure, like ADLS, ADF, Synpase Analytics, Databricks, Azure DevOps, or an equivalent stack
• Strong analytical, data profiling, and problem-solving skills