Enterprise Data Architect

Role: Enterprise Data Architect

Location: Remote

We are seeking a Data Architect(Go Getter Attitude) who operates at the intersection of data modeling, business understanding, and analytical insight. This role is not limited to designing schemas it requires a deep curiosity about how the business works and the ability to translate that understanding into scalable, coherent data domains and models that serve both analytical and operational needs.

Key Responsibilities

• Lead the design and evolution of enterprise data models, including conceptual, logical, and physical layers.
• Translate complex business processes, entities, and relationships into well-structured data domains and models.
• Partner closely with business stakeholders to understand workflows, metrics, and decision-making processes, and reflect them accurately in data structures.
• Develop models that support both analytical use cases (e.g., reporting, BI, data science) and non-analytical use cases (e.g., operational systems, APIs).
• Establish and enforce data modeling standards, naming conventions, and governance practices across the organization.
• Collaborate with data engineers to ensure models are implemented efficiently in modern data platforms (e.g., warehouses, lakehouses).
• Analyze existing data systems to identify gaps, redundancies, and opportunities for simplification or improvement.
• Drive domain-oriented data architecture (e.g., data mesh principles) where appropriate, ensuring clear ownership and boundaries.
• Ensure data models are scalable, maintainable, and aligned with long-term business strategy.

Qualifications

• Strong experience in data modeling (dimensional, normalized, and hybrid approaches).
• Demonstrated ability to deeply understand business domains and map them into data structures.
• Proficiency with SQL and experience working with modern data platforms (e.g., Snowflake, BigQuery, Databricks).
• Solid analytical skills, with the ability to explore and validate data against real business scenarios.
• Experience collaborating with both technical teams and non-technical stakeholders.
• Familiarity with data governance, metadata management, and data quality frameworks.
• Ability to think abstractly about systems while also engaging with detailed data-level implementation.