Senior Data Engineer (Snowflake)

Role & Responsibilities:

● 4+years of experience in Snowflake Development: Design, develop, and optimize data structures within Snowflake on Azure, utilizing Snow pipe, Snowpark, Stored Procedures, UDFs, and Streams & Tasks.

● Pipeline Orchestration: Build and manage complex ETL/ELT data pipelines using Azure Data Factory (ADF), incorporating robust error handling, alerting, and CDC methods.

● Data Platform Management: Work proficiently within the Azure data ecosystem, utilizing Azure Data Lake Storage (ADLS) and integrating Azure Key Vault for secure credential management.

● Advanced Querying: Develop complex SQL logic and perform query optimization and data manipulation across data warehouse and staging environments.

● Data Security & Governance: Implement Snowflake security configurations, including Role-Based Access Control (RBAC) and Data-Masking features.

● DevOps & Integration: Manage CI/CD pipelines using Azure DevOps (Git and branching strategies) and integrate data streams via SFTP and REST APIs.

● Quality Assurance: Assist in developing automated testing frameworks for data pipeline validation and data quality monitoring.

● Data Modeling: Apply dimensional modeling concepts (Star and Snowflake schemas) to build efficient data structures for financial reporting.

● Financial Alignment & Collaboration: Support the integration of financial data from platforms like Sage Intacct GL, SkyGen, and ClearWater; collaborate with stakeholders to gather technical requirements.

● Team Mentorship: Lead, mentor, and conduct code reviews for Junior Data Engineers to ensure alignment with engineering standards.

Skills expectation:

● Snowflake Expertise (Advanced): Strong hands-on experience with Snowflake on Azure, including performance tuning, virtual warehouses, data loading, RBAC, Data-Masking, and Snowpark.

● Azure Data Services: Extensive experience with Azure Data Factory (ADF) for complex pipeline orchestration, alongside proficiency in ADLS and Azure Key Vault.

● SQL & Programming: Expert-level SQL skills for data manipulation and query optimization, combined with intermediate Python skills for scripting and data processing.

● Data Warehousing & Modeling: Solid understanding of core data warehousing concepts, ETL/ELT patterns, and dimensional modeling.

● DevOps & Testing: Experience implementing CI/CD pipelines using Azure DevOps, version control (Git), and automated pipeline testing.

● Domain Awareness: Good to have knowledge of financial processes and familiarity with data structures from Sage Intacct GL, SkyGen, or ClearWater.

● Collaborative Execution: Strong problem-solving skills with the ability to translate business needs into technical pipelines and guide junior team members.