Data Engineer

Responsibilities:

• Build and optimise scalable data pipelines using PySpark and SQL
• Design and implement ETL/ELT processes for batch and streaming data
• Develop data solutions using Databricks Lakehouse and Delta Lake
• Ingest and integrate data from internal and external sources (e.g. Kafka, CDC)
• Optimise Spark jobs and data workflows for performance, scalability, and cost efficiency
• Manage infrastructure and environments using Terraform (IaC)
• Ensure data quality, monitoring, and reliability
• Implement governance and access controls (e.g. Unity Catalog)
• Deliver clean, structured, and accessible data for analytics and business use
• Collaborate with cross-functional teams to support analytics, reporting, and AI/ML initiatives

Qualifications:

• Demonstrated experience in data engineering, with a proven ability to build scalable data solutions
• Strong proficiency in Python and SQL
• Hands-on experience with Apache Spark (including Structured Streaming)
• Experience with Databricks (Workflows, Delta Live Tables, Lakehouse architecture)
• Experience with cloud platforms (AWS, Azure, or GCP)
• Experience with Terraform or similar infrastructure-as-code tools
• Experience working with structured and semi-structured data (e.g. JSON)
• Familiarity with CI/CD, modular development, and code documentation
• Strong communication skills and ability to work independently with a high level of ownership
• Preferred experience with Databricks certifications (Associate or Professional) and exposure to data tools such as Kafka, DBT, or similar technologies
• Advantageous to have knowledge of Scala or other programming languages, as well as experience working in Agile development environments