Snowflake Data Architect

The ideal candidate will lead end-to-end architecture/solution design centered on Snowflake, enabling scalable, secure, and AI-ready data platforms for commercial real estate business and its stakeholders. This role requires technical expertise (includes analytics and AI tech stacks), domain understanding, executive communication capability, and hands-on leadership in complex modernization initiatives and product platforms.

Key Responsibilities

Platform Architecture & Design § Own the end-to-end Snowflake architecture: account/org topology, databases, schemas, warehouses, and multi-environment (dev/UAT/prod) strategy. § Design the data layering model (raw → staging/EDP → curated/semantic marts) for CRE domains: property, lease, availability, sales, organization, contact.

§ Define semantic views / semantic models over business data so BI and LLM/NL-query tools consume governed, business-friendly entities. § Set warehouse sizing, scaling, and workload isolation (ETL vs BI vs data-science vs app-serving). § Establish naming conventions, object ownership, and reference architecture patterns.

Data Modeling & Engineering Leadership § Define dimensional/data-vault or hybrid models and canonical join keys (e.g., a single property-building spine) to prevent fanout across property/lease/sale subject areas. § Guide ETL/ELT pipeline design (batch and streaming), including ingestion from CRM (Salesforce), market feeds, and third-party CRE sources.

§ Lead use of dynamic tables, streams, tasks, and Snowpark for transformation and incremental processing. § Set standards for data typing/casting, deduplication, and handling of wide, loosely-typed source tables. Data Governance, Security & Compliance § Architect RBAC: role hierarchy, functional/access roles, and segregation of duties between policy creation and application.

§ Implement column-level security (masking), row-level security (row access policies), object tagging, tag-based masking, and sensitive-data classification for PII (tenant/landlord/contact data, deal financials). § Define data quality monitoring (DMFs, expectations, anomaly detection) and auditing via Access History / Object Dependencies.

§ Align with regulatory/privacy requirements (GDPR/CCPA, client confidentiality, NDA-bound deal data) and enforce network/egress policies. Integration & Interoperability § Design integration patterns between Snowflake and downstream stores (e.g., Postgres), apps (Streamlit/React), and BI tools. § Govern external connectivity: External Access Integrations, API ingestion, Secure Data Sharing, and provider/consumer data exchange with brokers/partners.

§ Oversee geospatial and market-data enrichment pipelines (property coordinates, geocoding done upstream of egress-restricted layers). Performance, Cost & Reliability (Fin. Ops) § Optimize query performance: clustering keys, search optimization, pruning, and result/metadata caching. § Own cost governance: warehouse auto-suspend/resume, resource monitors, budgets, and credit-consumption attribution by cost center/project via tags.

§ Define SLAs, monitoring, alerting, and capacity planning; track serverless (DMF/Snowpark) spend. AI / Analytics Enablement § Enable natural-language querying and analytics on CRE data through semantic models and (where in-boundary) Cortex. § Partner with data science on feature stores, model data access, and governed access to sensitive attributes.

§ Ensure LLM/agent access respects masking, row policies, and agent-aware controls. DevOps, CI/CD & Operations § Establish Infrastructure-as-Code (schemachange/Terramform/dbt) and Git-based deployment for Snowflake objects and semantic models. § Define environment promotion, cloning strategy for test data, and release/rollback processes.

§ Implement observability (query history, account usage views) and operational runbooks. Strategy, Leadership & Stakeholder Management § Set the Snowflake roadmap and reference standards; evaluate new features and editions for adoption. § Act as design authority: review designs, mentor engineers, and enforce best practices.

§ Translate CRE business needs (brokerage, valuations, capital markets, property management) into platform capabilities. § Partner with business, security, and infra leaders; present trade-offs (e.g., direct-Snowflake vs replicated-store architectures) and manage vendor/partner relationships. Key Qualifications Experience: 15+ years of overall experience in data architecture, data engineering, or enterprise data platforms, with substantial hands-on Snowflake experience.

Proven ability to lead solution design, data modelling, integration, and implementation of scalable enterprise platforms.

Education

Bachelor’s or Master’s degree in Computer Science, Information Technology, Engineering, or a related field. Relevant Snowflake, cloud, or data architecture certifications are preferred. SQL, Python, and Performance Optimization: Advanced SQL skills covering complex queries, stored procedures, incremental processing, and query tuning.

Strong Python experience for data processing, API integration, and automation. Ability to analyze query profiles, optimize warehouse usage, and balance performance with cost. Core Snowflake Expertise : Hands-on expertise with Snowflake key features including Account/warehouse/database design, RBAC, resource monitors, multi-cluster warehouses.

Dynamic tables, streams, tasks, Snowpark (Python), semantic views/models. Secure Data Sharing, cloning, Time Travel, replication/failover. Snowflake governance suite: masking policies, row access policies, object tagging, tag-based masking, data classification, data quality (DMFs), Access History, Object Dependencies.

Cost/performance tuning: clustering, search optimization, pruning, caching, credit attribution. Data Modeling & Engineering : Strong dimensional modeling, Data Vault, and medallion/layered architecture patterns. Expert SQL and performance tuning; handling wide, loosely-typed source tables (casting, dedup, canonical keys to avoid fanout).

ELT/ETL design; experience with dbt and orchestration (Airflow/dbt Cloud/native tasks). CRE-relevant modeling: property/building spine, lease/availability/sales subject areas, CRM (Salesforce) integration. Integration & Programming : Ingestion from Salesforce/CRM, market-data feeds, APIs, files, and streaming. Python (Snowpark, pandas) and optionally Java/Scala; JavaScript for app tiers.

Integration with downstream stores (e.g., Postgres) and app/BI layers (Streamlit, React, Power BI/Tableau/Looker). Geospatial/market-data enrichment awareness (coordinates, geocoding pipelines). Security, Governance & Compliance : Deep RBAC design and segregation-of-duties modeling. PII protection for tenant/landlord/contact and deal-financial data; GDPR/CCPA and client-confidentiality/NDA handling.

Network security concepts: network policies, Private. Link, External Access Integrations, egress controls. DevOps / CI-CD / IaC : Git-based deployment and Infrastructure-as-Code for Snowflake (schemachange, Terraform, dbt). CI/CD pipelines (Azure DevOps/GitHub Actions), environment promotion, rollback, and test-data cloning.

Observability via Account Usage/Information Schema, logging, and alerting. Cloud & Ecosystem : Strong on at least one cloud (Azure preferred if deploying to Azure VMs/CICD, else AWS/GCP): storage, networking, VMs, key vaults, identity. Familiarity with containerization (Docker) and, ideally, Snowpark Container Services.

AI / Advanced Analytics (differentiator) : Experience enabling natural-language querying / semantic layers and governed LLM access. Snowflake Cortex and feature-store/ML data-access patterns; agent-aware security controls. Personal Strengths Leadership & Soft Skills : Design-authority experience: design reviews, standards enforcement, mentoring engineers.

Stakeholder management across business (brokerage, valuations, capital markets, property management), security, and infra. Ability to communicate architecture trade-offs and cost implications to technical and non-technical audiences. Fin. Ops mindset and strong documentation discipline. Communication: Ability to explain architecture, governance, security, and AI concepts clearly to technical teams and business stakeholders.

Stakeholder Management: Ability to collaborate with data owners, security teams, application teams, and business leaders to align requirements and resolve priorities. Architectural Leadership: Ability to define standards, evaluate design decisions, mentor engineers, and guide teams toward secure and maintainable solutions.

Delivery Management: Ability to manage multiple initiatives, dependencies, and timelines across distributed teams. Problem-Solving: Strong analytical judgment, with the ability to clarify ambiguous requirements, identify risks, and recommend practical solutions.