Solutions Architect

Data & Analytics Architect – Snowflake

The organization is initiating a data-platform modernization program for its Commercial Real Estate (CRE) analytics ecosystem. The engagement begins with a one-month discovery and assessment phase to evaluate existing systems, identify gaps, and define a future-state data platform on Snowflake
.

The Data & Analytics Architect will play a key leadership role by conducting technical discovery, guiding architecture decisions, and producing a clear modernization roadmap that incorporates enterprise data management and observability capabilities
.

• Lead technical and business discovery sessions to understand current data sources, workflows, and reporting processes
• Assess the current-state data architecture
  , including ingestion, transformation, storage, analytics, and data management practices
• Evaluate existing Data Quality (DQ), Data Governance (DG), Master/Reference Data Management (MDM), and Data Observability (DO) maturity
• Identify gaps related to scalability, data quality, governance, lineage, monitoring, and manual processes
• Design the future-state Snowflake-based data architecture
  , including Bronze/Silver/Gold layers
• Define how DQ rules, DG policies, MDM integration, and observability frameworks integrate with the Snowflake platform
• Recommend modernization strategies for legacy systems, spreadsheets, and siloed reporting
• Translate business requirements into clear architectural recommendations and trade-offs
• Produce key discovery deliverables such as:
• Current-state assessment
• Gap analysis (architecture + data management)
• Future-state architecture diagrams
• High-level modernization roadmap

• 10+ years of experience in data architecture, data engineering, or analytics architecture
• Strong hands‑on experience with Snowflake (data modeling, performance, security, cost optimization)
• Proven experience leading discovery or assessment phases for data modernization initiatives
• Solid understanding of enterprise data management concepts
  , including:
• Data Quality (DQ)
• Data Governance (DG) and stewardship models
• Master and Reference Data Management (MDM)
• Data Observability (monitoring, alerting, anomaly detection)
• Strong knowledge of ETL/ELT patterns, cloud data platforms, and analytical data modeling
• Ability to operate in ambiguous environments and drive clarity through structured assessment
• Strong stakeholder communication and documentation skills