Associate Principal Engineer

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.