About the Client
Our client is one of the leading insurance companies.
About the Role
Azure Data Engineer
We are strengthening our capability to bring data science solutions into production faster and with higher quality. This role bridges the gap between data science and data/platform engineering by taking prototypes and turning them into scalable, governed solutions.
This is not a pure data engineering role — your focus is bringing machine learning solutions into production.
Responsibilities
-Take data science prototypes (Python / notebooks) and productionise them on Databricks
-Design and implement robust data and ML pipelines
-Package and deploy models into production-grade workflows
-Apply MLOps practices (versioning, monitoring, deployment)
-Ensure solutions are scalable, reliable, and maintainable
-Co-manage Databricks environments, jobs, and workflows
-Apply data governance, security, and access controls aligned with standards
-Collaborate closely with data scientists to prepare models for production use
-Reduce dependency on heavy handovers by owning implementation within the team
More info about the role:
-You will be embedded in a data science team
-Typical setup of 2–3 Data Scientists + 1 Production Engineer
-Focus on production models and ensuring they run reliably
Requirements
-4-5 year experience in data engineering or backend engineering roles
-Strong hands-on skills in Python and SQL
-Experience with Databricks, Spark, and Delta Lake
-Exposure to machine learning workflows and concepts
-Experience deploying solutions into production environments
-Understanding of data modelling, performance optimisation, and data quality
Nice to Have Skills
-Experience with Azure ecosystem (ADF, Storage, etc.)
-Familiarity with MLOps practices or tools
-Experience with APIs or real-time data solutions
-Background in insurance or regulated environments