Data Architect

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
Capability
Relevant Knowledge And Experience
Architecture
Enterprise data architecture; solution design; data modeling; Data Warehouse; Data Lake; Lakehouse; Medallion patterns
Engineering
Python; SQL; ETL/ELT; APIs; automation; testing; code review; troubleshooting; performance tuning
Platforms
Azure, AWS or GCP; Snowflake; BigQuery; Redshift; relational and document databases
Governance
Data quality; metadata; lineage; cataloging; classification; MDM; privacy; security
Delivery
CI/CD; DataOps; DevSecOps; observability; migration; stakeholder management; technical mentoring
AI readiness
AI/ML data foundations; RAG; vector search; MLOps; Responsible AI controls

Work arrangement
No