Data Engineer – AWS & Snowflake

Hadoop Migration | Banking Modernisation Program

We are seeking an experienced Data Engineer to support a major banking data modernisation program, migrating existing Hadoop data workloads and pipelines to AWS and Snowflake.

This is a hands-on engineering role focused on building reliable data pipelines, transforming and migrating complex datasets, and delivering secure, scalable and production-ready data solutions within a highly regulated banking environment.

What You’ll Do

• Analyse existing Hadoop datasets, pipelines, transformations, dependencies and workload characteristics.
• Build and migrate batch, incremental and near-real-time data pipelines into AWS and Snowflake.
• Develop ingestion, transformation and integration solutions across raw/landing, curated and business-consumption layers.
• Re-engineer Hadoop-based workloads into scalable cloud-native data solutions.
• Develop Snowflake databases, schemas, tables, views, stages, Snowpipe, Streams and Tasks.
• Implement ETL/ELT pipelines and enterprise data models supporting analytics and reporting.
• Perform data profiling, cleansing, validation, reconciliation and migration testing.
• Implement data quality, lineage, metadata and governance requirements.
• Apply security controls including IAM/RBAC, encryption, masking, privacy and audit logging.
• Optimise Snowflake queries, warehouse sizing, workload performance and consumption costs.
• Build automated CI/CD and DataOps deployment processes.
• Implement monitoring, alerting, logging and operational-support capabilities.
• Produce technical designs, data mappings, pipeline documentation and operational runbooks.
• Work closely with data architects, AWS and Snowflake specialists, security teams, analysts and managed-service teams.

What You’ll Bring

• Strong hands-on data engineering experience with Snowflake and AWS.
• Experience migrating Hadoop-based data platforms, datasets and pipelines to cloud environments.
• Strong Snowflake development skills across databases, schemas, warehouses, tables, views, stages, Snowpipe, Streams and Tasks.
• Strong experience building batch, incremental and near-real-time ingestion pipelines.
• Advanced SQL skills and experience with Python, PySpark or similar data engineering technologies.
• Strong knowledge of ETL/ELT, data integration, data warehousing and lake/lakehouse concepts.
• Experience with dimensional and relational modelling, curated data products and semantic layers.
• Experience with AWS data ingestion, storage, processing and integration services.
• Knowledge of Hadoop technologies such as HDFS, Hive, Spark and related ecosystem tools.
• Experience with data quality, reconciliation, metadata, lineage, cataloguing and governance.
• Knowledge of IAM/RBAC, encryption, masking, privacy controls and secure data engineering.
• Experience with Git, CI/CD, DataOps and automated testing and deployment.
• Strong Snowflake performance optimisation and cloud cost-management skills.
• Experience delivering production monitoring, observability, operational readiness and support documentation.
• Banking, financial services or highly regulated enterprise experience is strongly preferred.
• Snowflake and AWS data engineering certifications are highly regarded.
• Experience with Power BI consumption patterns, enterprise semantic models or data-governance platforms would be advantageous.

Why Apply?

This is an opportunity to contribute to a significant Hadoop-to-AWS-and-Snowflake transformation, solve complex data engineering challenges and help build a modern enterprise data platform within a major banking environment.

If you are a hands-on Data Engineer with strong Snowflake, AWS and data migration experience, we would welcome your application.