Deputy Manager

• Implement and champion Data Mesh principles, treating data as a product, establishing clear data contracts, and enabling self-serve data platform capabilities across business domains.
• Design, build, and maintain high-performance analytical data models using robust dimensional modeling principles (Star Schema, Snowflake Schema) to support enterprise BI, analytics and AI/ML models.
• Develop exceptional Analytics data products using streaming and batch ingestion patterns in GCP.
• Design and Build AI agents that automate data engineering tasks (e.g., pipeline monitoring, anomaly detection, data quality remediation, natural-language data querying)
• Integrate LLMs with internal data systems, APIs, and tools via RAG pipelines, function/tool calling, and vector databases 
• Build agent memory, planning, and reasoning capabilities to support multi-step task execution 
• Implement guardrails, evaluation frameworks, and monitoring for agent reliability, safety, and cost control 
• Be the Subject Matter Expert in Data Engineering, AI integrations, and GCP services.
• Demonstrate technical knowledge/leadership skills and advocate for technical excellence.
• Work in a collaborative environment including pairing and mobbing with other cross-functional engineers.
• Work on agile teams to build modern data warehouses and deliver data products.
• Work effectively with data engineers, product owners, data stewards, and other technical experts.

Primary Skills Required:

• Experience in analyzing complex data, organizing raw data, and integrating massive datasets from multiple data sources to build domain-specific, reusable, and secure "data products" within a Data Mesh framework.
• Expertise in modern data modeling methodologies, specifically dimensional modeling (Star Schema, Snowflake Schema), and optimizing schemas for cloud-native data warehouses like BigQuery.
• Proven experience building and deploying AI agents or LLM-powered applications in production (not just prototypes)
• Experience working in an implementation team from concept to operations, providing deep technical subject matter expertise for successful deployment. Implement methods for automation of all parts of the data pipeline to minimize labor in development and production.
• Experience working with architects to evaluate and productionalize appropriate GCP tools for data ingestion, integration, presentation, and reporting.
• Experience working with all stakeholders to formulate business problems as technical data requirements, identifying and implementing technical solutions while ensuring key business drivers are captured in collaboration with product management.
• Experience designing and deploying pipelines with automated data lineage, robust data quality frameworks, and data observability. Identify, develop, evaluate, and summarize Proof of Concepts to prove solutions. Test and compare competing solutions and report out a point of view on the best solution.
• Experience with data governance, cataloging, and access control integration (e.g., GCP Dataplex, GCP Data Catalog) in a decentralized environment.

Experience Required:

• In-depth understanding of Google Cloud Platform and underlying architectures.
• 6+ years of Data engineering and analytics application development experience.
• 3+ years of building and deploying AI agents or LLM-powered applications in production
• Experience working in Google Cloud Platform (GCP) services: Big Query, Dataflow, Dataform, Astronomer, Data Fusion, Dataproc, Cloud Composer/Air Flow, Cloud SQL, Compute Engine, Cloud Functions, Cloud Run, Artifact Registry, GCP APIs, Cloud build and App Engine, and real-time data streaming platforms like Apache Kafka, GCP Pub/Sub and Dataplex.
• 5+ years of SQL development experience (including advanced optimization and analytical queries).
• 2+ years of professional development experience in Java or Python, and Apache Beam.
• 2+ years of designing and building Tekton or similar CI/CD pipelines.
• Extracting, Loading, Transforming (ELT/ETL), cleaning, and validating data.
  • Designing pipelines and architecture for data processing.