Responsibilities

• Work as part of a multidisciplinary team in a large and complex data environment to develop and deliver end-to-end data solutions.
• Translate user requirements and data models into effective, robust data engineering solutions in line with build standards and ensure code is production ready.
• Deliver data engineering solutions in line with project goals and delivery cycles, while working openly and collaboratively in our team.
• Be proactive and want to make an impact, and this may sometimes involve challenging current methods.
• Help build and maintain our data engineering community to facilitate knowledge sharing, innovation, and collaboration across the department.
• Work with new technologies such as Azure cloud-based tech stack, Databricks, Azure Data Factory, Agile ways of working, and practices such as CI/CD and DevOps.

Qualifications

• Designing and building robust and re‑usable data engineering solutions on any of the following cloud platforms - Azure, AWS, Google Cloud.
• Working with common data types and industry standard architectural patterns such as data warehouses/lake houses etc. - to service operational and/or analytical business needs.
• Proficiency in one or more of the following - SQL, Python, Apache Spark (Pyspark, Spark SQL).
• Performing troubleshooting, root‑cause analysis and improving data quality in the data products.
• Delivering through Agile/DevOps working practices in multi‑disciplinary teams - CI/CD, Scrum, Automation.
• Staying up‑to‑date with latest tools and technologies and building proof‑of‑concepts using those technologies to solve a business problem.
• Experience with Databricks, Informatica DEI and Azure Data Factory.
• Experience of implementing dimensional models.