Job Description

• Develop and maintain data pipelines that ingest and transform data from internal systems and external platforms.
• Build and maintain ETL/ELT workflows using Databricks, SQL, and Python/PySpark.
• Support ingestion of structured and semi-structured datasets into the data platform.

Data Platform & Warehouse Development

• Help maintain and improve the Databricks-based data warehouse.
• Contribute to curated datasets used by analytics, reporting, and data science teams.
• Assist in developing scalable and reliable data models.

Data Integration

• Support integration of data from internal databases and third-party applications (e.g. marketing platforms, product analytics tools, and operational systems).
• Ensure reliable and consistent data availability across the platform.

Data Quality & Reliability

• Implement and maintain data quality checks and monitoring to ensure accurate and reliable datasets.
• Investigate and resolve data pipeline failures and anomalies.
• Support monitoring and operational stability of data workflows.

AI-Assisted Engineering

• Use AI-assisted development tools to support coding, debugging, documentation, and pull request reviews.
• Contribute to improving engineering productivity through automation and modern development practices.

Machine Learning & Fraud Model Support

• Assist in building and maintaining data pipelines used for machine learning models, including fraud detection systems.
• Support data scientists with preparing and transforming datasets used for model training and evaluation.
• Contribute to reliable data availability for ML workflows.

Reporting & Analytics Support

• Work with reporting teams to provide reliable datasets used in Power BI dashboards and analytics.
• Support analysts with data discovery and transformation requirements.

Performance & Optimisation

• Assist in optimising queries and transformations to improve performance and reduce processing costs.

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Industry: IT / Hardware

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Keyskills