principal Gen ai engineer-Java Backend

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 inAnalytics, 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.