Data Modeler / Data Engineer
Summary
Architect and build end-to-end data pipelines, cloud data platform architectures, SQL layers, and BI semantic models with a focus on financial data integration and data-quality validation frameworks.
- Architect and build end-to-end data pipelines across companies with assorted source systems: REST API extractions, scheduled and incremental loads, database replication feeds, and flat-file/ERP ingestion
- Land and stage raw source data in modern cloud data platforms (lakehouse/warehouse architectures), with orchestration, scheduling, monitoring, and failure recovery
- Build and tune the SQL layer: load orchestration, reporting views, incremental vs. full-refresh strategies, performance optimization
- Develop semantic models and measures for BI consumption; manage deployments, refresh schedules, and access security
- Integrate financial data from accounting systems and reporting packages; reconcile operational data against finance-reported actuals
- Build validation and data-quality frameworks: automated tie-outs against source-system reports, pipeline health monitoring, and exception reporting that routes fixes back to operating teams
Skills and Experience:
- Expert SQL (complex queries, tuning, stored procedures) and strong dimensional-modeling fundamentals
- Hands-on experience with a modern cloud data platform (e.g., Microsoft Fabric, Databricks, Snowflake) - lakehouse/warehouse patterns, pipelines, notebooks
- API integration: REST extraction, auth flows (OAuth, service principals), pagination and rate-limit handling; scripting in Python
- Proven data-validation discipline - numbers tie to source before anyone has to ask