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.
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.
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.
Collaborate closely with Practice Owners and Business Leaders to align engineering capabilities with client demands and emerging technology trends.
Technical Requirements & Qualifications:
12–15+ 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
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
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)
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
