Data Engineer

SUMMARY:
-

POSITION INFO:

We are seeking a skilled Data Engineer to support the design, development, and maintenance of scalable data solutions within a banking environment.

The role will focus on building reliable data pipelines, integrating data from multiple sources, supporting analytics and reporting, and ensuring data is accurate, secure, and available for business decision-making.

What you'll do:

• Design, build, test, and maintain data pipelines and ETL/ELT processes.
• Extract, transform, and load data from multiple source systems.
• Work with data scientists, analysts, BI developers, architects, and business stakeholders.
• Develop and optimise SQL queries, stored procedures, and data transformation logic.
• Support data integration between operational systems, data warehouses, lakes, and reporting platforms.
• Build and maintain data models to support analytics, reporting, and downstream consumption.
• Monitor data pipelines for failures, performance issues, and data quality concerns.
• Troubleshoot and resolve data-related production issues.
• Support data governance, security, access control, and compliance requirements.
• Document data flows, data definitions, technical designs, and support procedures.
• Contribute to automation, performance optimisation, and continuous improvement of the data platform.
• Support data analysis requirements to enable business insights and decision-making.

Your Expertise:

• 3+ years’ experience as a Data Engineer, BI Data Engineer, ETL Developer, Data Warehouse Developer, or similar.
• Strong hands-on experience with SQL.
• Experience building and maintaining ETL/ELT pipelines.
• Experience working with large datasets and multiple source systems.
• Experience with data warehousing, data lakes, or lakehouse environments.
• Experience with tools such as Azure Data Factory, Databricks, Synapse, Microsoft Fabric, AWS Glue, Redshift, BigQuery, Snowflake, or similar would be advantageous.
• Experience with Python, PySpark, Spark, Scala, or similar would be beneficial.
• Understanding of data modelling, dimensional modelling, and data transformation principles.
• Experience with data analysis, data exploration, and supporting analytical use cases.
• Experience with data quality checks, reconciliation, and pipeline monitoring.
• Banking, fintech, payments, risk, fraud, customer analytics, or financial services experience would be advantageous.

Technical Skills

• SQL / T-SQL
• Data Analysis
• ETL / ELT
• Data pipelines
• Data warehousing
• Data lakes / lakehouse
• Data modelling
• Python / PySpark
• Spark / Databricks
• Azure Data Factory
• Azure Synapse
• Microsoft Fabric
• Snowflake
• BigQuery / Redshift
• APIs / file ingestion
• Git / CI/CD
• Data quality
• Data governance
• Power BI integration