Enterprise Data Architect
First seen by Alion on Sep 2, 2026.
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 :
- 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.
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 - 3 of the following modern platforms : Databricks (Delta Lake, Unity Catalog, Spark), Snowflake (Snowpark, Streamlit, Data Sharing), Google BigQuery, or 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.
Skills
Data Engineering, Data Architect, ETL, DataLake, Data Warehousing, Databricks, Snowflake DB, .Net, Java, Kafka, RDBMS