OverviewWe’re looking for a skilled

Data Analytics Engineer

to help drive the evolution of our clients data platform. This role is ideal for someone who thrives on building scalable data solutions and is confident working with modern tools such as

Azure Databricks ,

Apache Kafka , and

Spark . In this role, you’ll play a key part in designing, delivering, and optimizing datapipelines and architectures. Your focus will be on enabling robust data ingestion and transformation to support both operational and analytical use cases. If you’re passionate about data engineering and want to make a meaningful impact in a collaborative, fast-paced environment, we want to hear from you!

Role and Responsibilities

Designing and building scalable datapipelines using

Apache Spark

in

Azure Databricks

Developing real-time and batch data ingestion workflows, ideally using

Apache Kafka

Collaborating with data scientists, analysts, and business stakeholders to build high-quality data products

Supporting the deployment and productionisation of

machine learning pipelines

Contributing to the ongoing development of a

Lakehouse

architecture

Working in an

Agile/DevOps

environment to continuously improve platform performance and reliability

Essential Skills and ExperienceWe’re seeking candidates who bring strong technical skills and a hands-on approach to modern data engineering. You should have:

Proven experience with

Azure Databricks

and

Apache Spark

Working knowledge of

Apache Kafka

and real-time data streaming

Strong proficiency in

SQL

and

Python

Familiarity with

Azure Data Services

and CI/CD pipelines in a DevOps environment

Solid understanding of data modelling techniques (e.g., Star Schema)

Excellent problem-solving skills and a high attention to detail

Experience working with unstructured data sources (e.g., voice)

Exposure to Power BI for downstream reporting (desirable, but secondary to platform engineering skills)

Previous experience in regulated industries

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