Requirements
- Experience : 9+years
- Relevant experience in data engineering, database development, or cloud data platforms.
- Strong hands-on experience with Snowflake architecture, data loading, SQL development, performance optimization, and security.
- Strong proficiency in advanced SQL, including complex joins, window functions, stored procedures, query profiling, and troubleshooting.
- Strong Python programming skills for data processing, automation, API integration, and error handling.
- Hands-on experience developing and maintaining data pipelines using Azure Data Factory or comparable integration tools.
- Practical experience with Snowflake Cortex AI, including Cortex Analyst, Cortex Search, and AI agent creation.
- Experience with Snowpark for data transformation, processing, and application integration.
- Strong understanding of ETL/ELT patterns, incremental loading, CDC, data validation, and data quality management.
- Experience designing and maintaining dimensional and relational data models, semantic views, and semantic models.
- Understanding of natural-language-to-SQL, semantic search, keyword search, embeddings, text chunking, and RAG concepts.
- Experience integrating Snowflake AI capabilities with enterprise applications through APIs and Python.
- Knowledge of Snowflake security and governance features, including RBAC, masking policies, row access policies, and auditing.
- Experience with Git, Azure DevOps, version control, and automated deployment practices.
- Strong analytical, troubleshooting, problem-solving, and communication skills.
- Ability to collaborate effectively with cross-functional teams and translate business requirements into scalable data and AI solutions.
- Experience mentoring junior engineers and following coding standards, documentation practices, and code review processes.
Responsibilities
- Design, develop, and maintain scalable Snowflake data architectures to support data storage, processing, reporting, analytics, and AI-driven use cases.
- Develop and optimize ETL/ELT pipelines using advanced SQL, Python, Snowpark, Azure Data Factory, and other integration tools such as SnapLogic.
- Integrate structured and semi-structured data from databases, APIs, enterprise applications, and files, and prepare unstructured content for AI processing.
- Implement full-load, incremental-load, and Change Data Capture (CDC) processes with appropriate data validation, reconciliation, error handling, and recovery mechanisms.
- Develop and maintain dimensional and relational data models aligned with business requirements.
- Build, configure, and optimize Snowflake tables, views, stored procedures, tasks, streams, and dynamic tables.
- Analyze query profiles and optimize warehouse sizing, clustering, partition pruning, and caching to improve performance and control costs.
- Review and enhance SQL and Python code to improve efficiency, security, reliability, and maintainability.
- Implement data quality checks to ensure data completeness, accuracy, consistency, validity, and deduplication.
- Design and implement AI-enabled data solutions using Snowflake Cortex AI, Snowpark, Cortex Analyst, Cortex Search, and AI agents.
- Apply Snowflake Cortex AI functions to use cases such as text summarization, classification, information extraction, and data enrichment.
- Develop semantic views and semantic models with clear business definitions and validated analytical queries to support natural-language analytics.
- Implement AI-driven capabilities involving natural-language-to-SQL, semantic and keyword search, embeddings, text chunking, and Retrieval-Augmented Generation (RAG).
- Integrate Snowflake AI capabilities with enterprise applications using APIs and Python.
- Collaborate with business stakeholders to identify and deliver practical AI use cases across property, lease, availability, client, and market data.
- Implement Snowflake security and governance controls, including role-based access control (RBAC), masking policies, row access policies, and auditing.
- Monitor pipeline execution, data freshness, warehouse utilization, failures, and platform performance to ensure reliable production operations.
- Establish logging, alerting, retry, and recovery mechanisms for data pipelines and AI-enabled solutions.
- Maintain version-controlled code and support automated deployments using Git and Azure DevOps.
- Document data architectures, mappings, pipelines, semantic definitions, and operational procedures.
- Collaborate with application engineers, data analysts, architects, and business stakeholders throughout the development lifecycle.
- Mentor junior engineers, conduct code reviews, and promote engineering best practices and continuous improvement.
Bachelor’s or master’s degree in computer science, Information Technology, or a related field.