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.

Department: Engineering
Function: Information Technology
Experience Level: Not Applicable