Data Architect

Role purpose

The Data Architect will define scalable, secure and governed datasolutions while remaining actively involved in development and delivery. Therole combines enterprise architecture leadership with hands-on engineeringacross data integration, cloud platforms, databases, data quality and AI-readydata foundations. The successful candidate will translate business needs intopractical designs, reusable patterns and production-quality solutions, workingclosely with product, engineering, analytics, security and DevOps teams.

What you will do

Architecture, design and technical leadership

·   Define and evolve dataarchitecture roadmaps, reference architectures, standards and reusable designpatterns aligned with business priorities.

·   Design conceptual, logical andphysical data models, including dimensional, relational, document andanalytics-ready models.

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

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

·   Translate business and productrequirements into implementable solution designs, delivery increments andtechnical guardrails.

Hands-on engineering and delivery

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

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

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

·   Build reusable APIs, dataservices, integration components and proof-of-concepts; contribute productioncode where the solution requires senior technical ownership.

·   Perform code and designreviews, 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 datasolutions across Azure, AWS and GCP, including secure storage, compute,networking and platform integration patterns.

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

·   Implement CI/CD, automatedtesting, deployment, monitoring and infrastructure automation using DataOps andDevSecOps practices.

·   Define observability, alertingand performance-tuning approaches across databases, pipelines, warehouses andcloud services.

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

Data governance, quality and security

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

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

·   Design security controlsincluding role-based access, encryption, data masking, row- and column-levelcontrols, and secure handling of sensitive data.

·   Ensure solutions comply withapplicable CBRE policies, architecture standards and regulatory requirements inpartnership with security and governance teams.

Analytics, AI and intelligent data solutions

·   Design analytics-ready datamarts, semantic models and reporting layers for Power BI, Tableau andself-service analytics.

·   Create trusted, AI-ready datafoundations for model training, inference and advanced analytics, includingreusable datasets and feature-engineering pipelines.

·   Design Retrieval-AugmentedGeneration, vector search, document ingestion, embedding, indexing andenterprise knowledge-retrieval patterns where required.

·   Support secure integration ofenterprise data with cloud AI services, copilots and intelligent assistantswhile applying Responsible AI, privacy, security and governance controls.

·   Partner with Data Scientistsand ML Engineers on MLOps patterns for model deployment, monitoring, driftdetection and operational reliability.

Collaboration and delivery accountability

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

·   Mentor engineers anddevelopers, improve engineering practices, and communicate complex architecturedecisions to technical and non-technical stakeholders.

·   Evaluate emerging technologiesthrough focused proof-of-concepts and recommend adoption only where measurablebusiness or engineering value is demonstrated.

Required experience and capabilities

·   Bachelor’s degree in computerscience, Engineering, Information Systems or a related discipline, orequivalent practical experience.

·   15+ years of overallexperience in Data engineering and enterpriseplatforms.

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

·   Significant experiencedesigning enterprise data platforms and delivering data engineering solutionsin complex, multi-team environments.

·   Demonstrated hands-ondevelopment experience with Python and advanced SQL, including performanceoptimization and production support.

·   Practical experience with dataintegration, data modeling, Data Warehouse, Data Lake and Lakehousearchitecture.

·   Experience with at least onemajor cloud platform and modern cloud data services; ability to applyarchitecture principles across Azure, AWS or GCP.

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

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

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

Preferred experience

·   Hands-on experience withSnapLogic 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 dataplatforms, RAG, vector databases, semantic search, MLOps or enterprisecopilots.

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

Core skills