Data Modelling

Tata Consultancy Services; Columbus (OH); On-site; USD 120,000 - 130,000; Full time

Benefits offered by this job

Discretionary Annual Incentive

Medical Coverage

Parental Leaves

401K Plan

Certification & Training Reimbursement

Job summary

Tata Consultancy Services in Columbus, OH, is seeking a data modeling professional to define entities, keys, and table grains for enterprise analytics. The role emphasizes dimensional modeling, metadata, validation, and history-aware design to support AI/BI pipelines. Strong knowledge of Databricks and Erwin is preferred.

On-site position with competitive compensation. Salary range reflects market standards for the region and role, with comprehensive benefits and a focus on data architecture

Qualifications

• Define entities, relationships, and keys (primary/foreign keys) and document cardinalities.
• Specify table grain (row-level meaning) for each fact/event/feature table.
• Create dimension tables with consistent codes, descriptions, and hierarchy attributes.
• Create fact/event tables with measures, timestamps, and join keys to relevant dimensions/entities.
• Represent time using standard date/time fields and effective start/end dates for history.
• Standardize naming conventions, data types, units, and allowed values (domains).
• Add metadata and documentation (field definitions, source system, refresh cadence, owner).
• Implement basic validation checks (nullability, uniqueness, referential integrity, value ranges).
• Design outputs for common access patterns (joins, filters, aggregations) used by OLAP and AI pipelines.
• Maintain versioning/change history for models and update downstream dependencies when schemas change.
• Optimize the models for performance of AI/BI use cases.
• Good understanding of Databricks concepts.
• Good understanding of tools like Erwin.

Responsibilities

• Define entities, relationships, and keys (primary/foreign keys) and document cardinalities.
• Specify table grain (row-level meaning) for each fact/event/feature table.
• Create dimension tables with consistent codes, descriptions, and hierarchy attributes where applicable.
• Create fact/event tables with measures, timestamps, and join keys to relevant dimensions/entities.
• Represent time using standard date/time fields and, when needed, effective start/end dates for history.
• Standardize naming conventions, data types, units, and allowed values (domains) across models.
• Add metadata and documentation (field definitions, source system, refresh cadence, owner).
• Implement basic validation checks (nullability, uniqueness, referential integrity, value ranges).
• Design outputs for common access patterns (joins, filters, aggregations) used by OLAP and AI pipelines.
• Maintain versioning/change history for models and update downstream dependencies when schemas change.
• Optimize the models for performance of AI/BI use cases.
• Good understanding of Databricks concepts.
• Good understanding of tools like Erwin.

Skills

Define entities, relationships, keys

Table grain specification

Dimensional modeling

Fact/event tables design

Time-based history modeling

Standards & naming conventions

Metadata & documentation

Data validation rules

OLAP/AI data access patterns

Versioning & change history

Model optimization for AI/BI

Databricks concepts

Erwin tooling

Education

BACHELOR OF COMPUTER SCIENCE

Tools

Databricks

Erwin

Job description

Skills / Requirements

• Define entities, relationships, and keys (primary/foreign keys) and document cardinalities

Job Description

Must Have Technical/Functional Skills

• Define entities, relationships, and keys (primary/foreign keys) and document cardinalities

• Specify table grain (row-level meaning) for each fact/event/feature table
• Create dimension tables with consistent codes, descriptions, and hierarchy attributes where applicable
• Create fact/event tables with measures, timestamps, and join keys to relevant dimensions/entities
• Represent time using standard date/time fields and, when needed, effective start/end dates for history
• Standardize naming conventions, data types, units, and allowed values (domains) across models
• Add metadata and documentation (field definitions, source system, refresh cadence, owner)
• Implement basic validation checks (nullability, uniqueness, referential integrity, value ranges)
• Design outputs for common access patterns (joins, filters, aggregations) used by OLAP and AI pipelines
• Maintain versioning/change history for models and update downstream dependencies when schemas change
• Optimize the models for performance of AI/BI use cases
• Good understanding of Databricks concepts
• Good understanding of tools like Erwin

TCS Employee Benefits Summary

• Discretionary Annual Incentive.
• Comprehensive Medical Coverage: Medical & Health, Dental & Vision, Disability Planning & Insurance, Pet Insurance Plans.
• Family Support: Maternal & Parental Leaves.
• Insurance Options: Auto & Home Insurance, Identity Theft Protection.
• Convenience & Professional Growth: Commuter Benefits & Certification & Training Reimbursement.
• Time Off: Vacation, Time Off, Sick Leave & Holidays.
• Legal & Financial Assistance: Legal Assistance, 401K Plan, Performance Bonus, College Fund, Student Loan Refinancing.

Salary Range $120,000-$130,000 a year

Qualifications

BACHELOR OF COMPUTER SCIENCE