Data Modeler

Position – Data Modeller

Location – Pune (Hinjewadi)

- Mumbai (Vikhroli)

Work Model – Hybrid

Shift Timings – General

Experience Required – 5 to 8 years

Notice Period – Immediate to 15 days

Experience

Overall, 5+ years of experience in data engineering and cloud migration in

large, complex organisations with min 3+ years into data modelling

Experience with Entity-Relationship (ER) diagrams for designing entities,

attributes, and relationships.

Knowledge of Object-Oriented Modelling (e.g., class structures, inheritance).

Knowledge of Relational Database Management System (RDBMS) concepts

Familiarity with Dimensional Modelling for analytics (facts and dimensions).

Understanding of Business Process Modelling to link workflows and data

entities.

Proficient with data modelling tools like SqlDBM and Medallion Architecture

(Bronze, Silver & Gold data layers) is preferable or with Erwin.

Proficiency in Semantic Modelling for ontology-based data organization.

Ability to translate conceptual models into logical database designs.

Strong skills in defining data flows, dependencies, and relationships.

Proficiency in normalization (1NF, 2NF, 3NF) for reducing redundancy and

improving data integrity.

Expertise in designing network and hierarchical models where needed.

Experience translating logical models into database schemas.

Knowledge of indexing, partitioning, and database optimization for

performance.

Familiarity with cloud-based database systems (e.g., Snowflake, Redshift).

Proficiency in handling structured, semi-structured, and unstructured data.

Star Schema design for simplified analytics and reporting.

Snowflake Schema experience for storage optimization.

Knowledge of Data Vault for historical tracking and scalability.

Expertise in designing models for BI tools (e.g., Power BI, ThoughtSpot,

Tableau).

Experience with Graph Modelling for social networks, fraud detection, or

interconnected data.

Proficiency in Document Modelling (e.g., JSON, BSON) for NoSQL databases

like MongoDB.

Knowledge of Key-Value or Wide-Column Stores for NoSQL use cases.

Familiarity with Temporal Data Models for handling time-variant or historical

data.

Proficiency in SQL for database design and query optimization.

Familiarity with modern data platforms and tools: Erwin, ER/Studio, or similar

for traditional modelling.

Familiarity with collaborative design tools like Lucid chart, dbForge, or

PowerDesigner.

Experience with automation & cloud data platforms like Snowflake, AWS,

Azure, or GCP.

Strong understanding of data governance and metadata management.

Hands-on experience with data integration and ETL/ELT processes.

Ability to design scalable models for both OLTP (transactional systems) and

OLAP (analytics systems).