Lead Data Modeller

• Lead conceptual, logical, and physical data modeling initiatives across enterprise data platforms and business domains.
• Establish and maintain data modeling conventions, naming standards, versioning strategies, and governance practices.
• Define domain boundaries, core entities, master/reference data structures, and authoritative systems for subject areas.
• Create model-to-contract mappings for APIs, data products, and Operational Data Store (ODS) tables.
• Collaborate with Data Owners and Data Stewards to secure approvals for data definitions, business rules, and change management processes.
• Contribute to enterprise governance forums, metadata management initiatives, and data lineage programs.
• Ensure glossary completeness and lineage capture using Dataplex and Data Catalog standards and conventions.
• Apply CIA-based data classification frameworks including Confidentiality, Integrity, Accountability, and Availability at attribute level.
• Define masking, pseudonymization, and role-based access control requirements for sensitive and PII data.
• Embed data-sharing principles, governance standards, and data contract rules into model deliverables.
• Shape Big Query physical models including partitioning, clustering strategies, constraints, and table/view optimization aligned to near real-time ODS requirements.
• Collaborate with platform teams to align metadata harvesting, domain structures, and data zone standards within GCP environments.
• Translate business rules into data quality checks, observability frameworks, and acceptance criteria integrated within source and ELT layers.
• Contribute to data quality dashboards, governance backlogs, and operational monitoring initiatives.
• Design reusable and governed data products with clearly defined SLAs, SLOs, service boundaries, and standardized API enablement strategies.
• Facilitate modeling workshops, steward reviews, and cross-domain design sessions while documenting architecture decisions and traceability.

• 6–8 years of experience in enterprise data modeling, data architecture, and governance initiatives.
• Strong expertise in conceptual, logical, and physical data modeling methodologies.
• Deep understanding of master data management, reference data structures, and enterprise data governance practices.
• Hands‑on experience designing data models for APIs, data products, and Operational Data Store environments.
• Strong knowledge of Big Query physical modeling, partitioning, clustering, and optimization strategies within GCP ecosystems.
• Experience with Dataplex, Data Catalog, metadata management, and data lineage frameworks.
• Strong understanding of data privacy, data security, masking, pseudonymization, and compliance principles.
• Experience implementing data quality frameworks, observability solutions, and governance standards.
• Knowledge of enterprise data product design, SLA/SLO management, and API enablement strategies.
• Strong analytical, stakeholder management, communication, and facilitation skills.
• Ability to collaborate effectively with architects, governance teams, data stewards, platform teams, and business stakeholders.

• Design scalable, governed, and enterprise-grade data models across complex business domains.
• Translate business requirements into optimized and standardized data structures and governance models.
• Drive enterprise data governance, lineage, quality, and metadata management initiatives.
• Collaborate effectively across technical, governance, and business teams within large-scale data ecosystems.
• Ensure compliance, security, and operational consistency across enterprise data platforms.
• Facilitate data modeling workshops, governance reviews, and architecture alignment discussions.
• Optimize data structures and physical models for performance, scalability, and near real-time analytics requirements.
• Promote continuous improvement and modern data architecture best practices across enterprise environments.

• Opportunity to work on enterprise-scale data transformation and governance initiatives.
• Exposure to advanced GCP, Big Query, metadata management, and enterprise data architecture environments.
• Collaborative and innovation‑driven data engineering culture.
• Opportunities for continuous learning, technical growth, and professional development.
• Dynamic environment encouraging ownership, governance excellence, and strategic innovation.
• Exposure to large-scale enterprise data modernization and digital transformation programs.

Want to discuss this opportunity in more detail? Feel free to reach out.