Strong understanding of data modeling concepts, methodologies, and tools.

Experience with data modeling for diverse technology platforms including cloud, mobile, IoT, and blockchain.

Familiarity with database management systems (e.g., relational, NoSQL).

Knowledge of SDLC and Agile development practices.

Proficiency in modeling tools such as ERwin, PowerDesigner, or similar.

Preferred Skills:

Experience with data integration tools and ETL processes.
Knowledge of data governance and compliance standards.
Familiarity with cloud platforms (AWS, Azure, GCP) and how they impact data architecture.

Overall Responsibilities:

Collaborate with business analysts, data engineers, and stakeholders to understand data requirements and translate them into robust data models.
Design logical and physical data models optimized for performance, scalability, and maintainability.
Develop and maintain documentation for data structures, including data dictionaries and metadata.
Conduct reviews of data models and code to ensure adherence to quality standards and best practices.
Assist in designing data security and privacy measures in alignment with organizational policies.
Stay informed about emerging data modeling trends and incorporate best practices into project delivery.
Support data migration, integration, and transformation activities as needed.
Provide technical guidance and mentorship related to data modeling standards.

Technical Skills (By Category):

Data Modeling & Data Management:Essential: Logical/physical data modeling, ER diagrams, data dictionaries.
Preferred: Dimensional modeling, data warehousing, master data management.
Programming Languages:Preferred: SQL (expertise in writing complex queries).
Optional: Python, R for data analysis and scripting.
Databases & Data Storage Technologies:Essential: Relational databases (e.g., Oracle, SQL Server, MySQL).
Preferred: NoSQL (e.g., MongoDB, Cassandra), cloud-native data stores.
Cloud Technologies:Preferred: Basic understanding of cloud data solutions (AWS, Azure, GCP).
Frameworks & Libraries: Not typically required, but familiarity with data integration frameworks is advantageous.
Development Tools & Methodologies:Essential: Data modeling tools (ERwin, PowerDesigner), version control (Git), Agile/Scrum workflows.
Security & Compliance: Knowledge of data security best practices, regulatory standards like GDPR, HIPAA.

Experience:

Minimum of 8+ years of direct experience in data modeling, data architecture, or related roles.
Proven experience designing data models for complex systems across multiple platforms (cloud, mobile, IoT, blockchain).
Experience working in Agile environments using tools like JIRA, Confluence, Git.
Preference for candidates with experience supporting data governance and data quality initiatives.
Note: Equivalent demonstrated experience in relevant projects or certifications can qualify candidates.

Day-to-Day Activities:

Participate in daily stand-ups and project planning sessions.
Collaborate with cross-functional teams to understand and analyze business requirements.
Create, review, and refine data models and associated documentation.
Develop data schemas, dictionaries, and standards to ensure consistency.
Support data migration, integration, and performance tuning activities.
Conduct peer reviews and provide feedback on data models and solutions.
Keep current with the latest industry developments in data architecture and modeling.
Troubleshoot and resolve data-related technical issues.

Qualifications:

Bachelors or Masters degree in Computer Science, Data Science, Information Technology, or related fields.
Demonstrated experience with data modeling tools and techniques in diverse technological environments.
Certifications related to data modeling, data management, or cloud platforms (preferred).

Professional Competencies:

Strong analytical and critical thinking skills to develop optimal data solutions.
Effective communication skills for translating technical concepts to non-technical stakeholders.
Ability to work independently and in collaborative team environments.
Skilled problem solver able to handle complex data challenges.
Adaptability to rapidly evolving technologies and project requirements.
Excellent time management and prioritization skills to deliver quality outputs consistently.

Engineer, Staff/Manager