Data Modeler | Warehousing 6 to 8+ yrs exp

Company Description QZENSYS is a trusted professional services partner providing IT staffing, consulting, software development, and technology solutions across modern platforms and tools. The company focuses on helping organizations build agile, future-ready teams by supplying specialized talent and expertise in cutting-edge technologies. QZENSYS emphasizes quality, reliability, and client success, supporting digital transformation initiatives and competitive growth in a rapidly changing technology landscape. In addition to staffing and consulting, QZENSYS delivers end-to-end software development services across diverse technology stacks and platforms, offering comprehensive support to its clients.
Interested folks please connect 9703589293.
JOB DESCRIPTION
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We are looking for an experienced Data Modeler
responsible for designing scalable, governed, and high-performance data
models for enterprise data platforms. The role will work closely with Data Architects, Data Engineers, BI developers, business
stakeholders, and application teams to translate business requirements into logical and physical data models.
The candidate should have strong experience in enterprise data modeling, dimensional modeling, data warehousing, cloud
data platforms, and modern Lakehouse architectures
Key Responsibilities
Design for enterprise data platforms.
Conceptual, Logical and Physical Data Models
Develop dimensional models
using Star and Snowflake schemas.
fact and dimension tables
Design , including conformed dimensions.
Define .
grain, keys, relationships, cardinality and referential integrity
SCD Type 1, Type 2 and other historical tracking approaches
Design .
Work with Data Architects to define enterprise data architecture and modeling standards.
Create models for environments.
Data Warehouse, Data Lake and Lakehouse
Develop data models for Bronze, Silver and Gold
layers in a Lakehouse architecture.
Optimize models for analytical workloads and BI reporting.
Work closely with Power BI/Tableau teams to create models suitable for semantic-layer consumption.
Analyze source systems and map source attributes to target data models.
Perform and define transformation rules.
source-to-target mapping
Identify data quality, duplication, normalization and data integrity issues.
Define metadata, business definitions and data lineage.
Support .
Data Governance, Master Data Management and reference-data modeling
Collaborate with Data Engineers during implementation and validate the implemented model.
Review SQL queries and database structures for performance and scalability.
EXPERTISE AND QUALIFICATIONS
Core Data Modeling
Conceptual, Logical & Physical Data ModelingER Modeling
Relational Data Modeling
Dimensional Modeling
Star/Snowflake Schema
Fact & Dimension Design
SCD Type 1 / Type 2
Normalization / Denormalization
Data Vault — good to have
Enterprise Data Modeling
Canonical Data Models
Master & Reference Data Modeling
Data Warehousing
Enterprise Data Warehouse
Data Marts
ETL/ELT architecture
Source-to-Target Mapping
Data Integration
Data Quality
Data Lineage
Cloud / Modern Data Platforms
Databricks / Delta Lake
Unity Catalog
Lakehouse Architecture
Azure Data Lake Storage
Azure Synapse
AWS Redshift
Snowflake — good to have
Microsoft Fabric — good to have
Modeling Tools
Experience with one or more:
ER/Studio
Erwin Data Modeler
Sparx Enterprise Architect
SAP PowerDesigner
SQL Developer Data Modeler
dbt
Databricks data-modeling capabilities

Work arrangement
Hybrid