Enterprise Data Architect

Paltech Consulting; Hyderabad; On-site; INR 4,000,000 - 6,000,000; Full time

Job summary

Paltech Consulting seeks a senior architect-level leader to design and scale data solutions across cloud platforms like Databricks, Snowflake, BigQuery, and Microsoft Fabric. You will define patterns integrating operational and analytical layers, and mentor engineers while leading architectural initiatives.

The role emphasizes hands-on architecture work, strong CI/CD, and experience with microservices, DDD, and event-driven systems.

Qualifications

• 12–15 years of progressive software engineering and enterprise architecture.
• Director/Principal/Chief Architect level experience managing complex multi-system environments.
• Advanced, deep-dive expertise in .NET Core and/or Java/Spring Boot.
• Microservices, domain-driven design (DDD), and distributed systems experience.
• Event-Driven frameworks and messaging (Kafka, RabbitMQ, AWS Kinesis) knowledge.
• Production-level knowledge of modern data platforms (Databricks, Snowflake, BigQuery, Microsoft Fabric).

Responsibilities

• Architect and scale modern data solutions across cloud platforms.
• Define patterns to integrate operational layers with analytical systems.
• Mentor senior engineers, tech leads, and architects; collaborate with leadership.
• Drive AI/automation with Agentic AI, RAG pipelines, and secure LLM orchestration.

Skills

.NET Core

Java/Spring Boot

Microservices

DDD

Distributed systems

CI/CD

Tools

Kafka

RabbitMQ

AWS Kinesis

Databricks

Snowflake

BigQuery

Microsoft Fabric

Kubernetes

Docker

Pinecone

Milvus

pgvector

Job description

Key Responsibilities

1. Architectural Strategy & Design

• Architect and scale modern data solutions (ETL/ELT pipelines, data lakes, and data warehouses) across multiple major cloud platforms like Databricks, Snowflake, BigQuery, or Microsoft Fabric.
• Define standard patterns for integrating operational application layers with analytical data systems seamlessly.

2. Hands-on Execution & Engineering Excellence

• Maintain a hybrid approach by remaining hands-on with architecture blueprints, system design, prototyping, and high-impact code reviews.
• Lead by example to champion clean code, robust CI/CD pipelines, containerization (Kubernetes/Docker), and reliable distributed systems.
• Troubleshoot complex architectural bottlenecks across application and data layers.

3. AI & Intelligent Automation

• Drive the production deployment of Agentic AI frameworks, autonomous workflows, and sophisticated prompt engineering protocols.
• Architect robust Retrieval-Augmented Generation (RAG) pipelines that bridge enterprise application microservices with large-scale analytical data stores safely and securely.

4. Technical Leadership & Calibration

• Mentor, guide, and upskill senior engineers, tech leads, and software architects.
• Collaborate closely with Practice Owners and Business Leaders to align engineering capabilities with client demands and emerging technology trends.

Technical Requirements & Qualifications

Core Experience

• 1215+ years of progressive experience in software engineering and enterprise architecture.
• Proven track record of operating at a Director, Principal Architect, or Chief Architect level, managing complex multi-system environments.

Application Engineering & Frameworks

• Advanced, deep-dive expertise in .NET Core and/or Java/Spring Boot.
• Extensive hands-on experience with Microservices architectures, domain-driven design (DDD), and distributed systems.
• Deep knowledge of Event-Driven Frameworks and messaging infrastructure (e.g., Kafka, RabbitMQ, AWS Kinesis).

Enterprise Data Ecosystems

• Deep, practical knowledge of Enterprise Data Architecture, data modeling, and robust ETL/ELT engineering.
• Production-grade, hands-on experience in at least 2 to 3 of the following modern platforms:
• Databricks (Delta Lake, Unity Catalog, Spark)
• Snowflake (Snowpark, Streamlit, Data Sharing)
• Google BigQuery
• Microsoft Fabric

Generative AI & Automation

• Foundational or production experience deploying Agentic AI systems (e.g., LangChain, AutoGen, CrewAI, or semantic kernels).
• Strong capability in structured prompt engineering, vector database implementation (e.g., Pinecone, Milvus, pgvector), and securing LLM-orchestrated applications.