The Snowflake Data Engineer is responsible for designing, building, and operating the Snowflake-based data and control layer that supports the Derivative Journal AI Agent. This role focuses on deterministic data processing, validation, reconciliation, and audit traceability, ensuring that AI-extracted data can be reliably transformed into Enertia-ready journal entry outputs suitable for finance and audit review.
The role is execution-focused and works closely with the Solution Lead to ensure accounting logic, validation rules, and acceptance criteria are correctly implemented and consistently applied.
Key Responsibilities
- Design and implement Snowflake schemas, tables, views, and transformations to support derivative statement ingestion, normalization, and processing.
- Develop canonical data models for derivative valuation data across multiple financial institutions.
- Implement deterministic validation, reconciliation, and calculation logic using Snowflake SQL and supporting constructs.
- Apply client-provided accounting mappings, sign logic, and transformation rules to produce Enertia-ready Excel/CSV outputs.
- Support ingestion and processing of AI-extracted data, ensuring separation between probabilistic extraction and deterministic accounting logic.
- Build and maintain audit artifacts, including source-to-output traceability, reconciliation summaries, and exception flags.
- Collaborate with the Solution Lead and client stakeholders to tune logic, resolve exceptions, and meet defined accuracy targets.
- Support close-cycle testing and stabilization, including troubleshooting data issues and refining validation thresholds.
- Implement basic operational controls such as logging, error handling, and repeatable execution patterns.
- Contribute to documentation, including data models, processing logic, and operational runbooks.
Required Skills & Experience
- Strong hands-on experience with Snowflake (schemas, SQL, views, tasks, stages, file formats).
- Advanced SQL skills with a focus on transformation, aggregation, and validation logic.
- Experience designing finance-grade data pipelines with reconciliation and audit requirements.
- Familiarity with ingestion patterns involving structured and semi-structured data (CSV, Excel, PDF-extracted outputs).
- Experience working with accounting or financial data, including debits/credits, sign logic, and reconciliation.
- Ability to translate business rules and accounting logic into deterministic data transformations.
- Experience supporting production data processes in close-cycle or time-sensitive environments.
Preferred Experience
- Prior work with derivative valuation, hedge accounting, or financial instrument reporting.
- Experience integrating AI-extracted or OCR-derived data into structured data platforms.
- Familiarity with Enertia or similar ERP systems (file-based integrations).
- Exposure to data quality frameworks, exception handling, and audit traceability patterns.
- Experience collaborating in fixed-bid or tightly scoped delivery environments.
Role Characteristics
- Execution-heavy, hands-on engineering role.
- Not responsible for defining accounting policy or financial interpretations.
- Works within clearly defined scope and acceptance criteria.
- Focused on reliability, correctness, and repeatability rather than experimentation.
- Supports a lean delivery model with high accountability.
Success Criteria
- Snowflake processing reliably produces Enertia-ready outputs that meet defined accuracy targets.
- Validation and reconciliation logic catches exceptions before downstream use.
- Audit traceability is clear, repeatable, and defensible.
- The solution operates consistently across counterparties and periods with minimal manual intervention.
- Phase I acceptance and Phase II scalability goals are met within agreed scope and timeline.