Role purpose

The Data Architect will define scalable, secure and governed data
solutions while remaining actively involved in development and delivery. The
role combines enterprise architecture leadership with hands-on engineering
across data integration, cloud platforms, databases, data quality and AI-ready
data foundations. The successful candidate will translate business needs into
practical designs, reusable patterns and production-quality solutions, working
closely with product, engineering, analytics, security and DevOps teams.

What you will do

Architecture, design and technical leadership

·   Define and evolve data
architecture roadmaps, reference architectures, standards and reusable design
patterns aligned with business priorities.

·   Design conceptual, logical and
physical data models, including dimensional, relational, document and
analytics-ready models.

·   Architect Data Warehouse, Data
Lake and Lakehouse solutions, including ingestion, storage, processing,
semantic and consumption layers.

·   Lead solution reviews and
technical decisions, balancing scalability, security, resilience, performance,
operability and cost.

·   Translate business and product
requirements into implementable solution designs, delivery increments and
technical guardrails.

Hands-on engineering and delivery

·   Design, build and optimize
batch, micro-batch and real-time ETL/ELT pipelines using SnapLogic, Informatica
and cloud-native integration services.

·   Develop Python-based ingestion,
transformation, validation, automation and reusable data-processing frameworks.

·   Write and tune SQL, stored
procedures, views and database objects across PostgreSQL, SQL Server, Oracle,
Snowflake, BigQuery and Redshift; support document-oriented solutions such as
MongoDB where appropriate.

·   Build reusable APIs, data
services, integration components and proof-of-concepts; contribute production
code where the solution requires senior technical ownership.

·   Perform code and design
reviews, troubleshoot complex data and performance issues, support releases,
and lead root-cause analysis for production incidents.

Cloud, platform and engineering practices

·   Design cloud and hybrid data
solutions across Azure, AWS and GCP, including secure storage, compute,
networking and platform integration patterns.

·   Guide legacy modernization and
data migration, including assessment, mapping, reconciliation, validation,
rollback and recovery considerations.

·   Implement CI/CD, automated
testing, deployment, monitoring and infrastructure automation using DataOps and
DevSecOps practices.

·   Define observability, alerting
and performance-tuning approaches across databases, pipelines, warehouses and
cloud services.

·   Optimize query execution,
indexing, partitioning, workload management, storage lifecycle and cloud
consumption.

Data governance, quality and security

·   Embed data ownership,
stewardship, metadata, cataloging, lineage, classification, retention and
Master Data Management practices into solution designs.

·   Implement data quality rules,
profiling, validation, reconciliation, exception handling, dashboards and
alerts using Collibra, SODA, Python and SQL.

·   Design security controls
including role-based access, encryption, data masking, row- and column-level
controls, and secure handling of sensitive data.

·   Ensure solutions comply with
applicable CBRE policies, architecture standards and regulatory requirements in
partnership with security and governance teams.

Analytics, AI and intelligent data solutions

·   Design analytics-ready data
marts, semantic models and reporting layers for Power BI, Tableau and
self-service analytics.

·   Create trusted, AI-ready data
foundations for model training, inference and advanced analytics, including
reusable datasets and feature-engineering pipelines.

·   Design Retrieval-Augmented
Generation, vector search, document ingestion, embedding, indexing and
enterprise knowledge-retrieval patterns where required.

·   Support secure integration of
enterprise data with cloud AI services, copilots and intelligent assistants
while applying Responsible AI, privacy, security and governance controls.

·   Partner with Data Scientists
and ML Engineers on MLOps patterns for model deployment, monitoring, drift
detection and operational reliability.

Collaboration and delivery accountability

·   Work across product, business,
engineering, analytics, security and operations teams throughout the solution
lifecycle.

·   Mentor engineers and
developers, improve engineering practices, and communicate complex architecture
decisions to technical and non-technical stakeholders.

·   Evaluate emerging technologies
through focused proof-of-concepts and recommend adoption only where measurable
business or engineering value is demonstrated.

Required experience and capabilities

·   Bachelor’s degree in computer
science, Engineering, Information Systems or a related discipline, or
equivalent practical experience.

·   15+ years of overall
experience in Data engineering and enterprise
platforms.

·   3+ years of experience in Analytics, AI and intelligent data solutions.

·   Significant experience
designing enterprise data platforms and delivering data engineering solutions
in complex, multi-team environments.

·   Demonstrated hands-on
development experience with Python and advanced SQL, including performance
optimization and production support.

·   Practical experience with data
integration, data modeling, Data Warehouse, Data Lake and Lakehouse
architecture.

·   Experience with at least one
major cloud platform and modern cloud data services; ability to apply
architecture principles across Azure, AWS or GCP.

·   Working knowledge of data
governance, quality, metadata, lineage, security and compliance controls.

·   Experience with CI/CD,
automated testing, monitoring, source control and modern engineering delivery
practices.

·   Strong analytical,
problem-solving and communication skills, with the ability to influence
technical decisions and work effectively across functions.

Preferred experience

·   Hands-on experience with
SnapLogic or Informatica, and platforms such as Snowflake, BigQuery, Redshift,
PostgreSQL, SQL Server, Oracle or MongoDB.

·   Experience with Collibra, SODA,
Power BI, Tableau, infrastructure automation, DataOps or DevSecOps.

·   Exposure to AI/ML data
platforms, RAG, vector databases, semantic search, MLOps or enterprise
copilots.

·   Relevant cloud, data
architecture, database or data engineering certifications.

Core skills