Data Modeler
Prudent Technologies and Consulting, Inc.
2 - 5 years
Hyderabad
Posted: 21/05/2026
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Job Description
Key Responsibilities
Data Modeling & Design
- Design, build, and maintain conceptual, logical, and physical data models for data warehouses, data lakes, and operational data stores.
- Apply dimensional modeling techniques (Kimball star/snowflake schemas, slowly changing dimensions, conformed dimensions) and Inmon-style normalized enterprise models as appropriate.
- Define naming conventions, modeling standards, and reusable design patterns; review peers models and provide constructive feedback.
- Partner with data engineers to translate models into performant physical implementations and ELT/ETL pipelines.
Requirements Gathering & Stakeholder Engagement
- Lead workshops with business stakeholders, product owners, and SMEs to elicit data requirements and translate them into clear modeling artifacts.
- Document business definitions, source-to-target mappings, lineage, and data quality rules.
- Act as a trusted advisor to analytics and engineering teams on the right modeling approach for a given use case.
Data Governance & Metadata
- Curate and maintain metadata, business glossary terms, and data classifications in the enterprise governance platform (e.g., Collibra).
- Support regulatory and compliance initiatives by ensuring sensitive data is properly classified, documented, and modeled.
Required Qualifications
- 812 years of professional experience in data modelling or a closely related data discipline.
- Proficiency with at least one industry-standard data modeling tool (e.g., erwin Data Modeler, ER/Studio, SAP PowerDesigner, SqlDBM).
- Excellent communication skills with the ability to explain complex data concepts to both technical and non-technical audiences.
Preferred Qualifications
- CDVP2 (Certified Data Vault 2.0 Practitioner) or equivalent Data Vault certification.
- CDMP (Certified Data Management Professional) or DAMA-DMBOK familiarity.
- Experience modeling for cloud data platforms (Snowflake, Databricks, BigQuery) and lakehouse architectures.
- Experience with dbt, Airflow, or similar transformation/orchestration tooling.
- Exposure to industry data models (e.g., FSLDM) relevant to the organizations domain.
- Familiarity with master data management (MDM), reference data, and data product / data mesh concepts.
- Experience supporting regulatory frameworks such as CCPA, HIPAA, or SOX.
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