Data Modeler

The Principal Data Modeler / Architect will lead a critical database modernization initiative to reverse engineer, redesign, and normalize our existing OLTP data architecture. This role requires a seasoned data professional who can analyze complex legacy systems, identify architectural debt, and execute a comprehensive schema refactoring strategy while ensuring zero business disruption. The ideal candidate combines deep expertise in data modeling, normalization principles, and large-scale database migration with strong collaboration skills to coordinate cross-functional teams through this transformation.

Experience in the EdTech domain is a plus.

Primary Objective

Reverse engineer our existing large-scale OLTP database, analyze current denormalized structures, and redesign the data model following normalization best practices while optimizing for performance, maintainability, and scalability in a modern SaaS environment.

Key Responsibilities

Database Analysis & Reverse Engineering

Reverse engineer existing OLTP databases to create comprehensive conceptual, logical, and physical data models

Analyze current schema design to identify denormalization patterns, data redundancies, integrity issues, and architectural debt

Profile existing data quality, identify anomalies, inconsistencies, and constraint violations

Document current state architecture, data flows, and integration points

Schema Redesign & Normalization

Redesign database schema by applying normalization principles (1NF through BCNF) to eliminate redundancy and improve data integrity

Create optimized conceptual, logical, and physical data models for the target state architecture

Design migration paths that maintain referential integrity and business rule enforcement

Identify opportunities for strategic denormalization where justified by performance requirements

Migration Strategy & Execution

Develop comprehensive migration strategies with minimal or zero downtime requirements

Design data transformation logic to migrate from denormalized to normalized structures

Create phased migration approaches using patterns such as strangler fig, dual-write, or shadow databases

Implement data validation and reconciliation frameworks to ensure migration accuracy

Develop rollback strategies and contingency plans for migration phases

Coordinate database deployment using CI / CD pipelines and migration tools

Performance Optimization & Tuning

Benchmark existing system performance to establish baselines

Optimize new schema design through indexing strategies, partitioning, and query optimization

Conduct load testing and performance validation pre and post-migration

Fine-tune database configuration, connection pooling, and caching strategies for AWS Aurora PostgreSQL

Ensure normalized design meets or exceeds current performance benchmarks

Data Governance, Security & Compliance

Ensure redesigned data model maintains compliance with relevant standards (GDPR, HIPAA, FERPA for EdTech)

Implement data governance policies, access controls, and security measures in new architecture

Define and enforce data quality standards throughout migration process

Establish data lineage and metadata management for new structures

Documentation & Knowledge Transfer

Create comprehensive documentation including data models, schema specifications, migration procedures, and data dictionaries

Develop data flow diagrams, entity-relationship diagrams, and architecture decision records

Document rationale for normalization decisions and any strategic denormalization

Create runbooks for migration execution and rollback procedures

Provide knowledge transfer sessions to technical teams

Required Skills & Qualifications

Data Modeling & Database Expertise

10+ years

of hands-on experience in data modeling and database design

5+ years

of experience reverse engineering and refactoring large-scale OLTP databases

Expert-level understanding of database normalization theory and practical application (1NF through 5NF, BCNF)

Deep expertise in both forward and reverse engineering of data models

Proven track record of successful database modernization and schema refactoring projects

In-depth knowledge of relational database design principles, patterns, and anti-patterns

Technical Proficiency

Expert-level SQL skills and extensive experience with PostgreSQL (required)

Proficiency with additional RDBMS platforms (SQL Server, Oracle, MySQL)

Hands-on experience with

AWS Aurora PostgreSQL

or similar cloud-based database platforms

Strong expertise in data modeling tools ( erwin, SqlDBM, ER / Studio, or similar )

Experience with database migration and version control tools

Familiarity with database comparison and schema diff tools

Knowledge of database DevOps practices and CI / CD pipeline integration

Experience with cloud platforms (AWS, Azure, GCP) and cloud-native database services

Relevant certifications (AWS Certified Database Specialty, PostgreSQL certifications) are advantageous