We are seeking an experienced Analytics Engineer / Data Engineer on a contract basis to lead the ingestion, modeling, and transformation of our core banking data into Snowflake. Our software team operates in a hybrid environment (on-premise Linux and Azure) using Python, GitHub, and Postman to integrate third-party vendor APIs. In this role, your primary objective will be modeling raw Jack Henry core banking data landed in Snowflake, transforming it into production-grade dimensional models using dbt and SQL, and appending key ancillary data sources (loans, deposits, digital banking, and compliance feeds) to create a unified enterprise analytics layer.
Key Scope of Work & Deliverables
" Jack Henry Core Data Modeling: Architect and build dimensional data models (Kimball star schema/snowflake schema) around Jack Henry core banking structures (accounts, transactions, customer profiles, general ledger).
" Ancillary Data Integration: Design dbt transformation pipelines that join and append external third-party data feeds to the Jack Henry core data foundation.
" dbt Pipeline & Testing: Develop, test, and document modular dbt transformation models, incorporating automated data quality checks and version control via GitHub.
" API Payload Transformation: Collaborate with our Python developers to process, flatten, and model raw JSON/relational API payloads landed from Jack Henry and other vendor interfaces.
" Snowflake Performance & Security: Optimize virtual warehouses, role-based access controls (RBAC), clustering, and query execution for banking analytics.
Required Qualifications & Skills
" Core Banking Domain: Direct experience working with Jack Henry core banking system data structures or similar core financial platforms.
" Snowflake & SQL: Deep expertise in Snowflake architecture, complex SQL, and handling semi-structured data (parsing JSON/VARIANT payloads).
" dbt Expertise: Proven track record building production dbt models, data quality tests, and documentation.
" Dimensional Modeling: Strong background in Kimball methodology, slowly changing dimensions (SCDs), and financial data modeling.
" Technical Stack Familiarity: Hands-on experience working alongside Python API ingestion workflows, GitHub version control, Postman API testing, and hybrid Linux/Azure environments