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.
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Analyse existing Hadoop datasets, pipelines, transformations, dependencies and workload characteristics.
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Build and migrate batch, incremental and near-real-time data pipelines into AWS and Snowflake.
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Develop ingestion, transformation and integration solutions across raw/landing, curated and business-consumption layers.
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Re-engineer Hadoop-based workloads into scalable cloud-native data solutions.
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Develop Snowflake databases, schemas, tables, views, stages, Snowpipe, Streams and Tasks.
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Implement ETL/ELT pipelines and enterprise data models supporting analytics and reporting.
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Perform data profiling, cleansing, validation, reconciliation and migration testing.
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Implement data quality, lineage, metadata and governance requirements.
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Apply security controls including IAM/RBAC, encryption, masking, privacy and audit logging.
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Optimise Snowflake queries, warehouse sizing, workload performance and consumption costs.
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Build automated CI/CD and DataOps deployment processes.
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Implement monitoring, alerting, logging and operational-support capabilities.
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Produce technical designs, data mappings, pipeline documentation and operational runbooks.
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Work closely with data architects, AWS and Snowflake specialists, security teams, analysts and managed-service teams.
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Strong hands-on data engineering experience with Snowflake and AWS.
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Experience migrating Hadoop-based data platforms, datasets and pipelines to cloud environments.
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Strong Snowflake development skills across databases, schemas, warehouses, tables, views, stages, Snowpipe, Streams and Tasks.
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Strong experience building batch, incremental and near-real-time ingestion pipelines.
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Advanced SQL skills and experience with Python, PySpark or similar data engineering technologies.
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Strong knowledge of ETL/ELT, data integration, data warehousing and lake/lakehouse concepts.
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Experience with dimensional and relational modelling, curated data products and semantic layers.
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Experience with AWS data ingestion, storage, processing and integration services.
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Knowledge of Hadoop technologies such as HDFS, Hive, Spark and related ecosystem tools.
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Experience with data quality, reconciliation, metadata, lineage, cataloguing and governance.
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Knowledge of IAM/RBAC, encryption, masking, privacy controls and secure data engineering.
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Experience with Git, CI/CD, DataOps and automated testing and deployment.
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Strong Snowflake performance optimisation and cloud cost-management skills.
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Experience delivering production monitoring, observability, operational readiness and support documentation.
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Banking, financial services or highly regulated enterprise experience is strongly preferred.
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Snowflake and AWS data engineering certifications are highly regarded.
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Experience with Power BI consumption patterns, enterprise semantic models or data-governance platforms would be advantageous.
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.
