Data Architect

·   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.