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 (FinOps)

§  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, PrivateLink, 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. FinOps 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.