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.