Snowflake Developer

Role Overview

Senior developer responsible for enhancing and maintaining the Snowflake analytics layer across selected data pipelines. The role focuses on translating business requirements into scalable data solutions, improving existing models, and ensuring high data quality and maintainability.

Key Responsibilities

• Develop and optimize source, aggregated, and consumer-facing data products in Snowflake.
• Translate evolving business requirements into new attributes, aggregation rules, drill-down capabilities, and planning-cycle versioning.
• Develop and maintain dbt models, tests, macros, and technical documentation.
• Implement data transformation and analytical logic, including status consolidation, rejection analysis, standard vs. actual effort comparisons, and basic statistical process control.
• Follow established standards for data modelling, naming, ownership, lineage, and documentation.
• Perform data validation, reconciliation, and support data-quality monitoring.
• Manage code through GitLab using branching, merge requests, CI/CD, and peer reviews.
• Participate in Agile ceremonies, estimation, demos, and UAT activities, including resolving defects and incorporating feedback.
• Contribute to technical documentation and knowledge transfer to the support team.

Requirements

• 3+ years of experience in data engineering, analytics delivery, or IT consulting.
• Strong hands-on experience with Snowflake, including advanced SQL, warehouses, roles, tasks/streams, and performance-conscious data modelling.
• Practical experience using dbt in production, including models, tests, macros, and documentation.
• Good understanding of Git workflows and CI/CD, preferably with GitLab.
• Experience enhancing and refactoring existing data pipelines based on business requirements and functional designs.
• Fluent English (B2/C1 or higher).

Nice to Have

• Experience in manufacturing, production, or yield analytics.
• Experience with R/Shiny or Streamlit for analytical solutions and prototypes.
• Knowledge of Tableau data-source modelling, extracts, published data sources, or row-level security.
• Exposure to SAP data extraction, Monte Carlo, Collibra, Immuta, Talend, or Airflow-like orchestration tools.