Job description

What you'll do: - Develop ETL pipelines (Python, SQL, PySpark) to integrate data from ERP, FP&A, and other enterprise systems into Microsoft Fabric. - Automate ingestion, transformation, and validation workflows to ensure accuracy, timeliness, and compliance of finance and accounting data. - Build and maintain Lakehouse structures (raw, processed, curated) to support efficient data modeling and reporting. - Curate datasets for end users and analysts to consume in their own reporting and dashboarding tools. - Design and maintain orchestration frameworks and dataflows to support higher-frequency refresh schedules while optimizing Fabric compute unit (CU) usage. - Support development and deployment of machine learning workflows (e.g., GL/expense classification models) as part of the financial reporting platform. - Collaborate with Finance, FP&A, and Product stakeholders to evolve data models and reporting capabilities. Required qualifications: - 3+ years of experience in data engineering or analytics with hands-on ETL pipeline development. - Proficiency in SQL, Python, and PySpark, with experience in Microsoft Fabric, Databricks, or Snowflake. - Understanding of data warehousing and Lakehouse architectures, medallion models, and orchestration workflows. - Experience with Azure or AWS cloud services (S3, EC2, IAM, Databricks, or Azure equivalents). - Familiarity with compliance-driven data handling, including audit readiness and government reporting requirements. Preferred qualifications: - Experience in a defense or government technology environment. - Exposure to finance systems (Costpoint, Vena, ERP/FP&A platforms) and accounting data structures. - Knowledge of compliance frameworks and data governance/security policies (DCAA, FAR/DFARS, CUI). - Background in automation or machine learning techniques to support data validation and compliance. - Experience delivering scalable data solutions in Agile environments.

Requirements