The Data Modeler is responsible for the hands-on design, development, and maintenance of logical and physical data models that support specific business applications and analytics requirements. This role translates the high-level data architecture strategy into detailed blueprints, ensuring data structures are efficient, consistent, and optimized for performance. The Data Modeler collaborates closely with data engineers and business analysts to create and implement data models that align with established governance standards and facilitate data-driven decision-making.
Data Modeling:
- Design and implement data models (dimensional, relational, Data Vault, NoSQL) to support operational and analytical use cases.
- Ensure consistency, accuracy, and integrity of data models across the organization.
- Continuously refine models to improve performance and adaptability to evolving business requirements.
- Establish and enforce data modeling standards and governance.
Data Engineering:
- Enhance data infrastructure to support efficient storage, retrieval, and transformation.
- Implement automation to reduce manual processes and increase workflow efficiency.
- Ensure data pipelines are robust, scalable, and support real-time and batch processing.
- Identify data discrepancies and develop data metrics.
Innovation & Technology Adoption:
- Stay up to date with new data modeling techniques and tools.
- Research and integrate cutting-edge tools and platforms to enhance data management capabilities and streamline processes.
- Encourage a team culture focused on continuous improvement, supporting experimentation with new technologies and methodologies.
- Develop and execute a technology roadmap to ensure the team is using the best tools available to meet both current and future data needs.
Collaboration with Cross-Functional Teams:
- Work closely with data scientists, business analysts, and BI teams to understand data requirements and design solutions that enable advanced analytics, reporting, and decision-making.
- Act as a bridge between business units and IT, ensuring that data architecture and engineering solutions meet both technical and business needs.
- Lead efforts to optimize data workflows and processes across departments, driving efficiency and alignment in data operations.
Educational Requirements: Bachelor’s or Master’s degree in Computer Science, Engineering, Information Systems, or a related field.
Advanced degrees provide deeper technical and strategic insights, enabling the professional to drive complex data solutions and innovations at the organizational level.
Special Certification or Training Required:
- Certified Data Management Professional (CDMP) – Preferred for expertise in data architecture and governance is preferred.
- Microsoft Certified: Azure Data Engineer Associate or AWS Certified Data Engineer Associate is preferred.
Required Industry Experience:
- 5+ years of experience in hands-on data modeling and data engineering, with a proven track record of designing and implementing data models for large-scale enterprise data initiatives.
- Expertise in designing, implementing, and optimizing data models and data ecosystems.
- Strong background in data governance, with a focus on enforcing data quality, consistency, and compliance standards.
- Hands-on experience in data integration, migration, and transformation projects within a large-scale business setting.
Technological Requirements:
- Extensive experience in various data modeling techniques, including conceptual, logical, and physical models, with a focus on dimensional and semantic modeling for analytics.
- Proficiency in data integration patterns, specifically in designing data models for streaming data platforms and event-driven architectures.
- Hands-on experience with modern cloud data platforms and their associated tools (Databricks, Snowflake, BigQuery, Synapse), including creating and optimizing data models within these ecosystems.
- Strong command of ETL/ELT frameworks and API-based data delivery, with the ability to design data structures that support efficient data pipelines.
- Practical experience with metadata management, data catalogs, and data quality frameworks, ensuring the integrity and discoverability of data models.
- Knowledge of data governance, lineage, and compliance requirements as they relate to data model design and implementation.
- Expertise in designing and optimizing data models for both relational (e.g., Oracle, SQL Server, PostgreSQL) and NoSQL (e.g., MongoDB, Cassandra) databases.
Language Requirements: Fluent in English.
