Data Engineer | $150k+

Feldspar & Flint LLC is a Recruiting & Staffing firm that specializes in operational strategy across core business functions.Our client is looking for a Data Engineer with 3+ years of experience to support and evolve a mortgage-focused data platform. This role combines ownership of a SQL Server-based data warehouse with contributions to a modern, Python-driven data stack. You will work closely with business stakeholders and engineering teams to ensure reliable, high-quality data pipelines and scalable data architecture.Key ResponsibilitiesOwn and optimize a SQL Server-based mortgage data warehouse, including performance tuning (queries, indexing, execution plans) and overall system reliabilityDesign and maintain ETL pipelines (SSIS and API-based) to ingest and integrate data from external servicers and third-party sourcesTranslate complex business requirements into scalable technical solutions, partnering closely with business stakeholdersManage daily and month-end data processing workflows, troubleshooting failures and ensuring consistent data availabilityStandardize, reconcile, and model data across multiple sources into analytics-ready data marts and support semantic layer development (e.g., SSAS Tabular)Contribute to modern data platform initiatives (Python, Spark, DuckDB, Polars, Delta Lake), including data quality frameworks, governance (metadata, lineage), documentation, and cloud-ready architectureRequired Qualifications3+ years of experience in data engineering, data warehousing, or a related fieldStrong hands-on experience with SQL Server (T-SQL, performance tuning, indexing strategies)Proven experience building and maintaining ETL pipelines, preferably using SSIS and/or Python-based frameworksSolid understanding of data modeling (dimensional modeling, data marts, normalization vs. denormalization)Experience integrating data from external systems via APIs or batch ingestionFamiliarity with data reconciliation and ensuring consistency across multiple data sourcesExposure to BI/semantic layers such as SSAS Tabular or similar technologiesProficiency in Python and experience working with modern data tools (e.g., Spark, DuckDB, Polars, Delta Lake)Strong problem-solving skills with the ability to debug production data issues