Snowflake Data Engineer

Job Description

CloudHive's customer is hiring an experienced Analytics Engineer / Data Engineer on a contract basis to own the ingestion, modeling, and transformation of core banking data into a cloud data warehouse.

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The engineering team works in a hybrid setup (on-premise Linux and a major cloud provider) using Python, Git-based version control, and standard API testing tools to integrate third-party vendor interfaces. Your main focus will be modeling raw core banking data landed in the warehouse, transforming it into production-grade dimensional models, and layering in key ancillary sources (loans, deposits, digital banking, and compliance data) to build a unified enterprise analytics layer.

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Key Scope of Work & Deliverables

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• Core Data Modeling: Architect and build dimensional models (Kimball star/snowflake schema) around standard core banking structures — accounts, transactions, customer profiles, and general ledger.

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• Ancillary Data Integration: Design transformation pipelines that join and append external third-party data feeds onto the core banking foundation.

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• Transformation Pipeline & Testing: Develop, test, and document modular transformation models with automated data-quality checks and version control.

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• API Payload Transformation: Partner with Python developers to process, flatten, and model raw JSON/relational API payloads from the core platform and other vendor interfaces.

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• Warehouse Performance & Security: Tune compute resources, role-based access controls (RBAC), clustering, and query execution for banking analytics workloads.

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Required Qualifications & Skills

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• Core Banking Domain: Direct experience with a major core banking platform's data structures or comparable financial systems.

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• Warehouse & SQL: Deep expertise in modern cloud data warehousing, complex SQL, and handling semi-structured data (parsing JSON/VARIANT payloads).

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• Transformation Tooling: Proven track record building production transformation models, data-quality tests, and documentation (dbt or equivalent).

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• Dimensional Modeling: Strong background in Kimball methodology, slowly changing dimensions (SCDs), and financial data modeling.

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• Technical Stack: Hands-on experience alongside Python-based API ingestion workflows, Git version control, API testing tools, and hybrid on-prem/cloud environments.

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