· Design and maintain scalable data pipelines aligned with lakehouse standards.

· Implement data integration from multiple systems into the lakehouse environment.

· Handle ingestion of structured/unstructured, streaming, and batch data efficiently.

· Apply transformations using Spark (preferably PySpark) and optimise workloads for performance and cost.

· Ensure data quality, governance, and consistency across datasets.

· Collaborate with cross-functional teams to support data analytics and reporting requirements.

· Contribute to improving architecture and workflow reliability.

Requirements

· Strong analytical skills with hands-on experience in SQL and Python (PySpark preferred).

· Solid understanding of data lake, Delta Lake, and lakehouse concepts.

· Familiarity with big data technologies such as Parquet storage formats and Unity Catalog.

· Experience with Spark and distributed data processing frameworks.

· Understanding of cloud data platforms and cluster/workload management.

· Experience using Databricks is considered an asset.

· Excellent verbal and written English communication skills.

· Previous experience collaborating with data engineering or analytics teams.

You will also need to:

· Demonstrate enthusiasm for working with large-scale data systems.

· Be proactive and adaptable in a collaborative environment.

· Think creatively and contribute to continuous improvement.