We are seeking an experienced Data Engineer to help organize, clean, and structure complex real estate and regulatory compliance data across multiple sources. This role focuses on transforming inconsistent datasets related to leases, occupancy, tenants, and rent information into a reliable and scalable data foundation. The ideal candidate will review existing data, identify quality issues such as duplication and missing fields, and design standardized schemas and relationships. You will build transformation workflows to clean and normalize data from spreadsheets, databases, and system exports. In this role, you will create master datasets for properties, units, households, leases, and compliance tracking while implementing validation rules and exception reporting. You will also document data definitions, mapping logic, and business rules to support transparency and long-term maintainability, while collaborating with stakeholders to translate operational requirements into structured data models. Strong proficiency in SQL and Python is required, along with hands-on experience in ETL/ELT workflows and relational data modeling. Experience working with messy, Excel-heavy datasets and building data quality checks is essential, and familiarity with tools like dbt, Airflow, or cloud platforms such as Snowflake or BigQuery is highly preferred. Success in this role means delivering a clear, consistent source of truth for lease and occupancy data, reducing inconsistencies, and preparing the data environment for reporting, automation, and future product development.
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