Overview

We are seeking an experienced Senior Snowflake Data Engineer to support the migration of a high‑volume MySQL database into Snowflake. In this role, you will design end‑to‑end Snowflake architecture, rebuild complex ETL/ELT workflows, and help implement modern engineering practices including CI/CD, automated testing, and version‑controlled SQL. This position is ideal for someone who thrives in data‑intensive environments and enjoys solving challenging ingestion and transformation problems at scale.
Client:
Our client is a global leader in the music and entertainment industry, known for its extensive portfolio of iconic artists, innovative digital initiatives, and large‑scale technology‑driven operations.
Project Overview:

Responsibilities:
  • Lead the migration of high‑volume MySQL workloads into Snowflake and design scalable Snowflake architecture
  • Build and optimize ETL/ELT pipelines for complex and irregular data files
  • Implement CI/CD, automated testing, and version‑controlled SQL for data workflows
  • Tune performance, manage cost optimization, and ensure reliable data processing at scale
  • Collaborate with cross‑functional teams and provide technical guidance to support high‑quality delivery
Required Qualifications:
  • 3+ years of hands‑on Snowflake engineering, including experience with tables, stages, file formats, tasks, streams, and Snowflake SQL
  • Strong understanding of ELT/ETL pipeline design in a cloud data warehouse environment
  • Proven experience migrating data from relational databases (MySQL, PostgreSQL, etc.) into Snowflake or similar platforms
  • Expertise with modern data transformation frameworks such as DBT, Airflow, Airbyte, or comparable tools
  • Proficiency in SQL and at least one scripting language (Python preferred)
  • Experience handling complex, messy, or irregular data file formats and building resilient ingestion solutions
  • Solid understanding of CI/CD for data workflows (GitHub Actions, Azure DevOps, GitLab CI, etc.)
  • Familiarity with data modeling best practices (Kimball, Data Vault, or multi‑layer staging/refined/gold patterns)
Note:

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