Job Description: Data Modeler
Role Summary
We are seeking an experienced Data Modeler with strong expertise in designing and delivering enterprise‑grade data models for cloud analytics platforms. Candidate has deep experience working in medallion architecture, cloud data warehouses (e.g., Snowflake), and has a solid background in data profiling, domain modeling, and enterprise data standards.
This role requires the ability to translate complex business requirements into scalable, high‑quality data structures that support analytical, operational, and reporting needs.
Key Responsibilities
Design, develop, and optimize enterprise data models (conceptual, logical, and physical) to support analytics, reporting, and downstream data products.
Build and maintain data structures aligned with medallion architecture (Bronze, Silver, Gold) within modern cloud ecosystems.
Conduct data profiling, source‑to‑target mapping, and domain modeling across multiple business domains.
Collaborate closely with data engineers, architects, and business SMEs to ensure data models meet functional and non‑functional requirements. Define and implement data modeling best practices, naming standards, and documentation frameworks.
Partner with cross‑functional teams to ensure scalability, performance, and alignment with the enterprise data strategy.
Support architecture reviews, data quality initiatives, governance processes, and metadata enrichment activities.
Provide modeling guidance for ingestion, transformation, and consumption layers within cloud analytics platforms.
Required Qualifications
7+ years of overall experience with of Minimum 3+ years of hands‑on experience as a Data Modeler for enterprise cloud data analytics platforms.
Strong experience with domain modeling, data profiling, and complex schema design across multiple business domains.
Deep understanding of medallion architecture and modern data lakehouse patterns.
Strong SQL skills and the ability to work with large‑scale datasets.
Experience collaborating with data engineering teams.
hands-on data engineering experience is a strong plus.
Excellent communication skills with the ability to work with both technical and non‑technical stakeholders.
Good to have:
Experience in commercial real estate (CRE) data domains, including property, leasing, transactions, valuations, asset management, or geospatial data. Familiarity with data governance, MDM, reference data platforms, and metadata standards. Exposure to geospatial datasets or address standardization frameworks (ESRI, GDAL, etc.) is a plus.
