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Mid-level metro data engineer with broad stack and popular title increases qualified applicant density.
Strong finance transformation requirements create high domain specificity despite transferable data engineering skills.
Explicit 4–6 years and specific Databricks/Snowflake skills enforce strict filters.
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Design and deliver production-grade, scalable data solutions supporting Finance Transformation functions such as Financial Reporting, Financial Close, Cash Forecasting, FP&A, and Order-to-Cash.
Build, optimize, and manage data pipelines and data migration using Databricks, Snowflake, Python, SQL, and PySpark within modern Lakehouse architectures.
Embed AI/GenAI capabilities into data workflows for intelligent automation, while ensuring data quality, security, and operational reliability across finance data processes.
4–6 years of experience in Data Engineering, Analytics, or Finance Transformation roles.
Strong hands-on expertise with Databricks, Snowflake, Python, SQL, and PySpark.
Experience with ETL/ELT frameworks, cloud-native data platforms, and data migration/modernization programs.
Knowledge of Finance processes such as Financial Reporting, Financial Close, FP&A, Cash Forecasting, Treasury, or Order-to-Cash.
Experienced in delivering enterprise-scale, production-ready data platforms specifically aligned to Finance Transformation and financial planning/forecasting functions.
Skilled in translating finance business process requirements into technical data solutions and AI-augmented automation.
Comfortable managing multiple stakeholders across business and technology teams with strong communication and problem-solving capabilities.