Role Overview
As a Data & AI Delivery Solution Architect, you will sit at the intersection of high-level strategy and hands-on execution. You are responsible for designing and leading the delivery of complex, scalable data platforms and cutting-edge AI/ML solutions.
This is a delivery-first role. While you will architect the vision, your primary focus is ensuring that the solution is buildable, performant, and delivers tangible business value. You will act as the technical bridge between stakeholders and engineering teams, navigating both AWS and Azure ecosystems to solve unique client challenges.
Key Responsibilities
1. Solution Architecture & Design
- Design end-to-end data architectures (Data Lakehouse, Mesh, or Fabric) leveraging AWS (Lake Formation, Glue, Redshift) and Azure (Synapse, Fabric, Databricks).
- Architect scalable AI/ML pipelines, focusing on MLOps and LLM-ops (Generative AI) using Amazon SageMaker and Azure Machine Learning.
- Select the right tool for the job, balancing cost, performance, and maintenance across multi-cloud environments.
2. Delivery Leadership
- Lead the technical delivery lifecycle from Discovery and PoC to Production-grade deployment.
- Provide hands-on leadership: Reviewing code, troubleshooting complex performance bottlenecks, and mentoring senior engineers.
- Define CI/CD strategies and IaC standards (Terraform/Bicep) to ensure repeatable and secure deployments.
3. Data & AI Strategy
- Advise clients on Modern Data Stack (MDS) trends, including Real-time streaming (Kinesis/Event Hubs) and Data Governance (Purview/DataZone).
- Design and implement Generative AI solutions using Azure OpenAI Service and AWS Bedrock, focusing on RAG (Retrieval-Augmented Generation) patterns.
4. Stakeholder Management
- Translate complex technical roadmaps into business-aligned value propositions for C-suite stakeholders.
- Manage technical risks and technical debt, ensuring project timelines are realistic and met.
Technical Qualifications
Core Cloud Expertise
- AWS: Deep experience with S3, Glue, Athena, Redshift, Lambda, and SageMaker.
- Azure: Expert knowledge of Azure Data Factory, Synapse Analytics, Azure Databricks, and Azure OpenAI.
- Data Structures: Mastery of Delta Lake, Iceberg, or Hudi formats.
Engineering & AI
- Programming: High proficiency in Python, SQL, and Spark (PySpark).
- AI/ML: Experience deploying Foundation Models, fine-tuning LLMs, and implementing vector databases (Pinecone, Weaviate, or Milvus).
- DevOps/DataOps: Experience with Git, Jenkins/ADO, and automated testing for data quality.
Soft Skills
- Ability to thrive in ambiguous, fast-paced consulting or enterprise environments.
- Excellent storytelling skillsthe ability to sell a technical vision to non-technical folks.
Preferred Certifications
- AWS Certified Solutions Architect Professional
- Microsoft Certified: Azure Solutions Architect Expert
- Databricks Certified Data Engineer Professional
