Technical Architect

Job Description:

Solution Architect (Snowflake, Python & SQL)

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Information Technology, Engineering, Data Science, or a related field.
• 10+ years of experience in Data Engineering, Data Warehousing, Data Architecture, or Solution Architecture.
• Minimum 5+ years of hands-on experience with Snowflake architecture, implementation, and optimization.
• Proven experience designing enterprise-scale cloud data platforms and analytics solutions.
• Strong experience with data modernization, cloud migration, and digital transformation initiatives.
• Experience leading technical teams and collaborating with cross-functional stakeholders.
• Excellent communication, presentation, and stakeholder management skills.

Required Skills

• Snowflake: Snowflake Architecture, Snowpark, Snowpipe, Streams & Tasks, Dynamic Tables, Data Sharing, Performance Tuning, Cost Optimization.
• Programming: Python, SQL, PySpark, Pandas, NumPy.
• Data Engineering: ETL/ELT Design, Data Pipelines, Data Warehousing, Data Lakehouse Architecture, Data Modeling.
• Generative AI & AI Technologies: Azure OpenAI, OpenAI APIs, LLMs, RAG (Retrieval-Augmented Generation), AI Agents, Prompt Engineering, Vector Databases, LangChain, Semantic Kernel.
• Cloud Platforms: Microsoft Azure (preferred), Azure Data Factory, Azure Databricks, Azure AI Services, Microsoft Fabric.
• DevOps & Automation: GitHub, Azure DevOps, CI/CD, Docker, Kubernetes, Terraform.
• Data Governance: Data Security, Data Quality Management, Metadata Management, Compliance, Access Controls.
• Visualization & Analytics: Power BI, Tableau, Advanced Analytics.

Key Responsibilities

• Design and implement scalable, secure, and high-performing enterprise data solutions using Snowflake and cloud technologies.
• Define end-to-end architecture for data ingestion, transformation, storage, governance, and analytics.
• Architect and optimize Snowflake environments to support business-critical workloads.
• Develop and review Python- and SQL-based solutions for data engineering and analytics use cases.
• Design and implement Generative AI solutions including LLM-powered applications, AI assistants, intelligent search, and enterprise copilots.
• Build and govern RAG-based architectures integrating structured and unstructured enterprise data.
• Collaborate with business stakeholders to convert business requirements into scalable technical solutions.
• Lead migration initiatives from legacy data platforms to Snowflake and cloud-native architectures.
• Establish best practices for data governance, security, performance optimization, and cost management.
• Provide architectural leadership, conduct design reviews, and mentor engineering teams.
• Evaluate emerging AI, cloud, and data technologies and recommend adoption strategies.
• Drive automation, innovation, and continuous improvement initiatives across data and AI platforms.
• Support solution estimation, technical planning, risk identification, and mitigation strategies.
• Create architecture documentation, technical roadmaps, and governance artifacts.
• Ensure compliance with enterprise standards, security policies, and regulatory requirements.

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