Data Engineer (Python, AWSAzure, Snowflake)

Job Description:

• Bachelors or Masters 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.

• 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.

• 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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