Sr Data Engineer

Role Overview We are seeking a Senior Data Engineer to lead the design and delivery of modern, cloud-based data solutions on Microsoft Azure and Snowflake. This role is responsible for building scalable data pipelines, developing efficient data models, and enabling high-performance, secure analytics for a wide range of business use cases. Key Responsibilities • Architect, develop, and maintain scalable data pipelines using Azure Data Factory and other Azure-native services • Design and manage Snowflake data structures, including databases, schemas, tables, views, streams, tasks, and stored procedures • Ingest and integrate data from multiple sources into Azure Data Lake Storage and Snowflake, supporting both structured and semi-structured formats • Establish and maintain data quality frameworks, including validation, error handling, and pipeline monitoring • Optimize performance and cost across Snowflake and Azure environments through query tuning, workload management, and resource configuration • Implement and enforce security and governance standards across Azure and Snowflake, including role-based access control and data protection practices • Partner with cross-functional stakeholders to translate business requirements into scalable data solutions • Support CI/CD processes for data engineering workflows using tools such as Azure DevOps or GitHub Actions Required Qualifications • Extensive hands-on experience with Snowflake, including advanced SQL development, performance optimization, and features such as Snowpipe, streams, tasks, and stored procedures • Proven experience building and maintaining production-grade data pipelines within the Azure ecosystem (especially Azure Data Factory) • Strong proficiency in SQL and at least one programming or scripting language such as Python or PowerShell • Deep understanding of data warehousing principles, dimensional modeling, and modern ELT/ETL practices • Experience working with Azure Data Lake Storage and integrating it with downstream analytics platforms • Familiarity with monitoring and logging tools such as Azure Monitor and Log Analytics, as well as Snowflake performance and query history tools • Experience supporting data pipelines for AI/ML workloads, including preparing datasets for training and inference Preferred Qualifications • Experience with additional Azure services such as Synapse Analytics, Databricks, or Azure Functions • Exposure to Azure AI/ML services, including Azure Machine Learning, Cognitive Services, or Azure OpenAI • Experience working in agile environments with strong collaboration across engineering, analytics, and business teams