Senior Data Engineer

Design, build, and maintain scalable data pipelines and data products using Databricks, PySpark, Spark SQL, and Delta Lake.

Develop and optimise ETL/ELT processes to ingest, transform, and curate data from internal and external systems.

Design and evolve enterprise data architecture, ensuring scalability, performance, security, and maintainability.

Partner with business stakeholders to understand requirements, translate business challenges into technical solutions, and identify opportunities to leverage data more effectively.

Ensure data quality, governance, security, and access controls are embedded throughout the data lifecycle.

Monitor, troubleshoot, and optimise data platform performance, reliability, and cost efficiency.

Enable analytics, reporting, AI, and self-service BI initiatives through trusted and accessible datasets.

Maintain engineering standards, CI/CD processes, technical documentation, and operational procedures.

Mentor junior team members and contribute to continuous improvement across the data function.

8+ years of experience in Data Engineering or a related field.

Strong hands-on experience with Databricks, including PySpark, Spark SQL, Delta Lake, Unity Catalog, and Lakehouse architecture.

Advanced SQL and Python development skills.

Proven experience building and optimising enterprise-scale data pipelines and ETL/ELT solutions.

Strong understanding of data architecture, data modelling, data warehousing, and modern data platform design.

Experience working with cloud platforms, preferably AWS.

Experience implementing data governance, security controls, and role-based access models.

Experience with source control, CI/CD, and software engineering best practices.

Experience with Databricks AI/BI, Genie, Genie Spaces, and Databricks Apps.