Data Architect

Description :

The Integration Services & Enablement product is designed to help Business Domains, Foundational domains and various Business embedded IT teams that are hesitant or lack subject matter expertise to build an integration solution. The professional services provide strategy, advice, and technical consultation for any  teams or stakeholders on their integration needs. Professional services contain integration architecture rules, best practices, patterns, and principles, as well as guardrails / governance that bridge all technological platforms to ensure and assist individuals in using the proper technology solution within the Digital Integration landscape.

We are building next-generation unified semantic layer. This is a unique opportunity to lead a critical initiative that will revolutionize how our teams access, understand, and utilize data across the organization. Person will be responsible for establishing the vision, defining the architecture, and guiding the team in building a centralized semantic model that powers our business intelligence, analytics, and data science efforts.

Technical Skills :

• Deep Understanding of Data Modeling Concepts : Expertise in conceptual, logical, and physical data modeling principles, including relational and dimensional modeling techniques.
• Experience with Semantic Layer Technologies : Hands-on experience with one or more semantic layer tools or frameworks (e.g., Cube.js, MetricFlow, LookML, AtScale, Microsoft Fabric Semantic Model, or similar).
• Proficiency in Data Manipulation and Querying : Strong SQL skills and familiarity with data manipulation techniques. Knowledge of other query languages (e.g., MDX, DAX) is a plus.
• BI Tool Expertise : Solid understanding of data modeling capabilities within Tableau or Thoughspot (or similar BI platforms) and how they can be leveraged by a central semantic model.
• API Integration : Experience with API development and integration for connecting the semantic layer with various systems.
• Data Governance and Security : Knowledge of data governance principles, data security best practices, and implementing access controls.
• Cloud Technologies : Familiarity with cloud data platforms (e.g., AWS, Snowflake) and their data warehousing and analytics services.
• Data Transformation (ETL / ELT) : Understanding of data transformation processes and tools (e.g., dbt).
• Scripting and Automation : Ability to write scripts in languages like Python for automation tasks.

Leadership and Soft Skills :