Data Architect:

About the Role

We're looking for a hands-on Data Architect who combines deep architectural thinking with real, hands-on engineering ability. This is not a "design-only" architect roleyou'll be expected to code, build, and guide pipelines yourself while also owning the bigger picture: architecture, standards, stakeholder alignment, and end-to-end delivery.

You'll bridge business needs with scalable, production-grade data platforms across Snowflake, Data Engineering, Data Quality, AI/ML enablement, Agentic AI solutions, and downstream analytics consumption.

Key Responsibilities

Architecture & Design

- Design and own end-to-end enterprise data architecture with Snowflake as the core data platform, leveraging Medallion (Bronze/Silver/Gold) architecture, Lakehouse patterns, and modern cloud data architectures.
- Define data modelling standards, ingestion strategies, governance, and storage/compute optimization across Snowflake, Databricks, Microsoft Fabric, and Azure.
- Architect scalable, secure, and high-performance solutions for BI, Analytics, AI/ML, Agentic AI, APIs, and enterprise applications.
- Establish enterprise standards for scalability, security, metadata management, lineage, observability, cost optimization, and governance.

Hands-On Engineering

- Personally build and develop production-grade data pipelines using SQL, Python, Snowpark, and PySpark.
- Design, develop, and optimize Snowflake-native ELT pipelines using Snowpipe, Dynamic Tables, Streams & Tasks, and Snowpark.
- Modernize legacy ETL pipelines into scalable cloud-native architectures following industry best practices.
- Build reusable APIs and data services for enterprise applications and downstream systems.
- Implement enterprise Data Quality frameworks with validation, reconciliation, anomaly detection, monitoring, and alerting.
- Optimize Snowflake warehouses, Spark jobs, Delta tables, and end-to-end pipeline performance.

Agentic AI & AI Enablement

- Design enterprise data architectures that enable AI Agents, Copilots, Retrieval-Augmented Generation (RAG), and LLM-powered applications.
- Build pipelines supporting vector search, embeddings, semantic search, and enterprise knowledge repositories.
- Integrate Snowflake Cortex AI, Cortex Search, Cortex Analyst, Azure AI Foundry, Microsoft AI Foundry, LangChain, LangGraph, Semantic Kernel, CrewAI, AutoGen, or similar AI orchestration frameworks.
- Design secure AI-ready data platforms with governance, lineage, RBAC, and metadata management.




- Develop ML-ready datasets and feature engineering pipelines supporting Machine Learning and Generative AI workloads.

Team Leadership & Stakeholder Management

- Mentor engineering teams on architecture, coding standards, performance optimization, and engineering best practices.
- Partner with business and product stakeholders to translate business requirements into scalable technical solutions.
- Own the complete Software Development Life Cycle (SDLC), including architecture, development, testing, deployment, monitoring, and production support.
- Drive architecture reviews, technical governance, and engineering excellence across the organization.

Platform & Ecosystem

- Work extensively across Snowflake, Microsoft Azure, Microsoft Fabric, Databricks, and modern cloud-native ecosystems.
- Build secure, governed, scalable, and AI-ready enterprise data platforms.
- Implement CI/CD, Infrastructure as Code (IaC), monitoring, logging, and observability across data platforms.

Required Skills & Experience

- Proven experience in Data Engineering and Data Architecture with strong hands-on development expertise.
- Expert-level SQL, Python, Snowpark, PySpark, and Apache Spark.
- Robust expertise in Snowflake, including Snowsight, Snowpark, Cortex AI, Cortex Search, Cortex Analyst, Snowpipe, Dynamic Tables, Streams & Tasks, CLI, Horizon Catalog, Tags, Data Sharing, and Performance Optimization.
- Strong expertise in Databricks, including Delta Lake, Unity Catalog, Delta Live Tables (DLT), Spark Optimization, Workflows, and MLflow.
- Strong understanding of Microsoft Fabric and Azure Data Platform.
- Deep expertise in Medallion Architecture, Lakehouse Architecture, Data Mesh, Data Vault, and modern ELT/ETL frameworks.
- Experience designing enterprise Data Quality frameworks using Great Expectations, Soda, Deequ, or custom frameworks.
- Experience developing REST APIs and enterprise data service layers.
- Strong understanding of AI/ML lifecycle, Feature Engineering, MLOps, LLM integration, and Agentic AI architectures.
- Experience implementing enterprise governance, metadata management, data lineage, RBAC, masking policies, and security frameworks.




- Experience with Git, Azure DevOps, GitHub Actions, CI/CD, automated testing, and release management.
- Excellent stakeholder communication, solution architecture, and technical leadership skills.

Technology Stack

Cloud & Data Platforms

- Snowflake (Snowpark, Cortex AI, Cortex Search, Cortex Analyst, Snowsight, Snowpipe, Dynamic Tables, Streams & Tasks, Horizon Catalog, Native Apps, Data Sharing)
- BigQuery
- Microsoft Azure
- Microsoft Fabric
- Databricks
- Azure Data Lake Storage Gen2 (ADLS Gen2)
- Azure Synapse Analytics

Data Engineering & Processing

- SQL
- Python
- Snowpark
- PySpark
- Apache Spark
- Delta Lake
- Delta Live Tables (DLT)
- Azure Data Factory (ADF)
- Microsoft Fabric Data Factory
- Apache Airflow

Data Architecture & Modeling

- Medallion Architecture (Bronze/Silver/Gold)
- Lakehouse Architecture
- Data Mesh
- Data Vault
- Star Schema
- Dimensional Modeling

Data Quality & Governance

- Snowflake Governance (Tags, Masking Policies, Row Access Policies, Horizon Catalog)
- Great Expectations
- Soda
- Unity Catalog
- Microsoft Purview
- Data Lineage
- Metadata Management

Agentic AI & AI/ML

- Snowflake Cortex AI
- Cortex Search
- Cortex Analyst
- Azure AI Foundry
- Microsoft AI Foundry
- Azure OpenAI
- OpenAI APIs
- LangChain
- LangGraph
- Semantic Kernel
- CrewAI
- AutoGen
- Model Context Protocol (MCP)
- Retrieval-Augmented Generation (RAG)
- Vector Databases (Azure AI Search, Pinecone, Weaviate, Milvus, ChromaDB)
- MLflow

APIs & Integration

- REST APIs
- FastAPI
- GraphQL
- Azure Functions
- Event Grid
- Azure Service Bus

DevOps & CI/CD

- Git
- GitHub
- GitHub Actions
- Azure DevOps
- Terraform
- Docker
- Kubernetes

BI & Analytics

- Power BI
- Tableau
- Microsoft Fabric Real-Time Intelligence
- Semantic Models
- DAX

Nice to Have

- SnowPro Core and SnowPro Advanced Certifications
- Databricks Certified Data Engineer Professional
- Microsoft Fabric Analytics Engineer (DP-600)
- Azure Data Engineer Associate (DP-203)
- Azure AI Engineer Associate (AI-102)
- Experience with Kafka, Azure Event Hubs, Apache Flink, or Spark Structured Streaming.
- Experience designing and implementing enterprise AI Agents, Multi-Agent Systems, MCP Servers, RAG applications, and Knowledge Graphs.
- Experience with Vector Databases such as Azure AI Search, Pinecone, Weaviate, Milvus, or ChromaDB.
- Experience working with enterprise data governance, FinOps, and cloud cost optimization across Snowflake and Azure.

📌 Data Architect (Telangana)
🏢 Prudent Globaltech Solutions
📍 Telangana