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

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

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

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

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

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

Hands-on engineering and delivery

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

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

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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.

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

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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

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

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

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

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

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

Data governance, quality and security

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

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

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

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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

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

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

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

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

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

Collaboration and delivery accountability

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

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

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

Required experience and capabilities

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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.

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

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

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

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

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

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

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

Preferred experience

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

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

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

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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