Role Description

Data Architect Role Overview The Data Architect will define and own end-to-end data architecture for AI initiatives on Google Cloud Platform (GCP). This role establishes the data models, platform patterns, governance, and operational standards needed to support ingestion, storage, transformation, serving, and retrieval. Working with business stakeholders, AI specialists, platform engineers, and data engineers, the Data Architect will translate business needs into scalable data foundations and clear architectural direction.

Key Responsibilities Data architecture and modeling: Own the architecture across BigQuery, AlloyDB/PostgreSQL, Cloud Storage, and related services. Lead conceptual, logical, and physical data modeling, including domain boundaries, partitioning, indexing, and schema evolution.

Pipeline architecture: Establish reference architectures and reusable design patterns for batch and streaming pipelines using Dataflow, Cloud Composer/Airflow, Pub/Sub, and Cloud Run. AI-ready data foundations: Design data foundations for retrieval-augmented generation (RAG), semantic search, and hybrid search, including document ingestion, chunking, metadata, embedding pipelines, vector storage using pgvector/ScaNN, and refresh and re-indexing patterns.

Large language model integration and evaluation: Define standards for integrating large language model (LLM) APIs, including Vertex AI/Gemini and other providers, into extraction, classification, summarization, and enrichment workflows. Balance quality, cost, latency, and security. Partner with quality assurance (QA) to design evaluation frameworks covering golden datasets, sampling, human review,



regression testing, and ongoing measurement of accuracy, consistency, cost, and latency.

Governance and data quality: Establish validation, cataloging, and lineage architecture, including tools such as Dataplex. Define standards for personally identifiable information (PII), retention, access controls, and auditability.

Reliability and operations: Set service-level agreement (SLA) and service-level objective (SLO) standards, monitoring, ing, idempotency, backfills, failure handling, and incident response. Lead architectural root cause analysis and post-incident reviews.

Technology strategy: Evaluate data technologies and platforms, document trade-offs, and guide build-versus-buy decisions, cost optimization, and scalability planning. Support integration with hybrid-cloud and legacy systems, including Azure-based data services where applicable. Cross-functional architecture: Partner with the AI Architect and AI Engineer to support retrieval and agent solutions through sound data design.

Work with the Platform Engineer on continuous integration and continuous delivery (CI/CD), Infrastructure as Code, and setting standards for data workloads.

Technical leadership: Mentor Data Engineers, conduct design and code reviews, and guide complex implementations.



Translate business needs and data domains into architectural roadmaps, and produce clear architecture documentation and decision records for technical and non-technical audiences.

Qualifications and Technical Expertise Experience designing end-to-end cloud data architectures, with expertise in GCP data services such as BigQuery, AlloyDB/PostgreSQL, and Cloud Storage. Strong conceptual, logical, and physical data modeling skills, including schema evolution, partitioning, indexing, and domain design.

Experience defining batch and streaming pipeline patterns using Dataflow, Cloud Composer/Airflow, Pub/Sub, and Cloud Run.

Experience designing data foundations for RAG, semantic and hybrid search, embedding pipelines, vector storage, and LLM-enabled workflows. Ability to define data quality, governance, lineage, security, retention, and access-control standards, alongside evaluation approaches for AI-enabled data use cases. Knowledge of data-platform reliability, monitoring, failure recovery, CI/CD, and Infrastructure as Code, with the ability to evaluate scalability, cost, and operational trade-offs.

Ability to lead architectural decisions, mentor Data Engineers, collaborate across business and engineering teams, and communicate technical direction through clear documentation and roadmaps. Familiarity with hybrid-cloud and legacy-system integration, including Azure-based data services where applicable.

Skills

AlloyDB, Google Cloud Platform, Data Modeling, Data Governance, LLMs, Vertex AI, Data Privacy, RAG, Observability, Google Cloud Dataflow, BigQuery

📌 Lead I - Product Architect (Chennai)
🏢 UST
📍 Chennai