Enterprise Data Architect

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.