Data Modeler 7+ years Immediate joiner only

Data modeler
7+ Years
Job Summary
We are looking for an experienced Data Modeler with strong expertise in conceptual, logical, and physical data modeling, dimensional modeling, data warehouse design, metadata management, and enterprise data architecture concepts.
The candidate should be capable of working closely with business stakeholders, Data Architects, and Data Engineers to translate business requirements into scalable and reusable data models.
Key Responsibilities
Design and maintain Conceptual, Logical, and Physical Data Models.
Develop enterprise and domain-level data models based on business requirements.
Design analytical models using Fact and Dimension structures.
Develop Star Schema and Snowflake Schema models for reporting and analytics.
Define entities, attributes, relationships, keys, constraints, and business rules.
Design and maintain SCD Type 1 and Type 2 modeling patterns.
Define appropriate use of surrogate keys, natural keys, composite keys, and referential integrity.
Perform normalization and denormalization based on transactional and analytical requirements.
Work with Data Architects and Data Engineers to ensure data models are aligned with the target architecture.
Review source systems and map source attributes to target data structures.
Prepare and maintain Source-to-Target Mapping documents.
Define naming conventions, data standards, and modeling guidelines.
Support data integration and ETL/ELT design by providing clear data model specifications.
Conduct data model reviews with technical and business stakeholders.
Identify duplicate or redundant data structures and recommend reusable enterprise models.
Maintain model versioning, documentation, metadata, and data dictionaries.
Support impact analysis for schema and business requirement changes.
Contribute to data governance, lineage, and metadata management initiatives.
Validate implemented database structures against approved data models.
Mandatory Skills
Data Modeling
Strong experience in:
Conceptual Data Modeling
Logical Data Modeling
Physical Data Modeling
Dimensional Modeling
Relational Modeling
Enterprise Data Modeling
Dimensional Modeling
Fact tables
Dimension tables
Star Schema
Snowflake Schema
Conformed Dimensions
Role-Playing Dimensions
Degenerate Dimensions
Junk Dimensions
Slowly Changing Dimensions
SCD Type 1 and Type 2
Transaction Fact Tables
Periodic Snapshot Facts
Accumulating Snapshot Facts
Grain definition
Relational Modeling
Normalization
1NF, 2NF, 3NF
Denormalization
Primary Keys
Foreign Keys
Candidate Keys
Natural and Surrogate Keys
Referential Integrity
Cardinality
Entity relationships
SQL Knowledge
Candidate should have strong SQL understanding to:
Analyze source data.
Validate relationships and data patterns.
Perform data profiling.
Identify duplicates and data quality issues.
Write joins, aggregations, CTEs, and analytical queries.
Validate implemented data models.
Perform source-to-target reconciliation.
Strong SQL development knowledge is preferred, though this is primarily a data modeling role rather than a data engineering role.
Data Architecture Knowledge
Good understanding of:
Data Warehouse Architecture.
Data Lake and Lakehouse concepts.
Medallion Architecture.
Enterprise Data Architecture.
ETL and ELT.
Operational vs Analytical data models.
Data Marts.
Semantic layers.
Master and Reference Data.
Metadata Management.
Data Lineage.
Data Governance.
Data Quality.
Cloud & Databricks Knowledge
Good to have exposure to:
Databricks Lakehouse Platform.
Delta Lake.
Unity Catalog.
Bronze, Silver, and Gold layers.
Databricks SQL.
AWS S3.
AWS DMS.
The candidate does not need to be a strong PySpark developer, but should understand how data models are implemented in modern cloud and lakehouse environments.
Modeling Tools
Hands-on experience with one or more modeling tools such as:
ER/Studio
erwin Data Modeler
SAP PowerDesigner
IBM InfoSphere Data Architect
SQL Developer Data Modeler
Hackolade
Visio / Lucidchart
Exposure to modern metadata/catalog tools is an added advantage.
Data Governance & Metadata
Good understanding of:
Business Glossary.
Data Dictionary.
Metadata Management.
Data Classification.
Data Lineage.
Data Ownership.
Data Stewardship.
Naming Standards.
Data Quality Rules.
Master Data and Reference Data concepts.
Experience with tools such as Collibra, Alation, Informatica EDC, AWS Glue Data Catalog, or Unity Catalog is good to have.
Business Analysis Skills
The candidate should be able to:
Understand business processes and business terminology.
Convert business requirements into data entities and relationships.
Conduct workshops with business and technical stakeholders.
Identify data domains and subject areas.
Define business definitions for key data elements.
Resolve ambiguity in source data and business rules.
Document assumptions and modeling decisions.
Preferred Candidate Profile
The ideal candidate should be a strong Data Modeler with enterprise and analytical modeling experience, capable of working across business, architecture, and engineering teams.
The candidate should have strong knowledge of Conceptual, Logical, Physical, and Dimensional Data Modeling, along with good SQL, metadata, governance, and modern cloud data platform exposure.

Work arrangement
Yes