Data Engineer: Python, Snowflake

Duties & Responsibilities

• Design, develop, and maintain scalable ETL/ELT data pipelines to support business and analytics needs
  
• Write, tune, and optimize complex SQL queries for data transformation, aggregation, and analysis
  
• Translate business requirements into well-designed, documented, and reusable data solutions
  
• Partner with analysts, data scientists, and stakeholders to deliver accurate, timely, and trusted datasets
  
• Automate data workflows using orchestration/scheduling tools (Airflow, ADF, Luigi, etc.)
  
• Develop unit tests, integration tests, and validation checks to ensure data accuracy and pipeline reliability
  
• Document pipelines, workflows, and design decisions for knowledge sharing and operational continuity
  
• Apply coding standards, version control practices, and peer code reviews to maintain high-quality deliverables
  
• Proactively troubleshoot, optimize, and monitor pipelines for performance, scalability, and cost efficiency
  
• Support function roll outs, including being available for post-production monitoring and issue resolution
  

Basic Qualifications

• Bachelor’s degree in computer science, Information Systems, Engineering, or a related field
  
• 2–5 years of hands-on experience in data engineering and building data pipelines
  
• At least 3 years of experience in writing complex SQL queries in a cloud data warehouse/ data lake environment.
  
• Solid hands-on experience with data warehousing concepts and implementations
  
• At least 1 year of experience with Snowflake or another modern cloud data warehouse
  
• At least 1 year of hands-on Python development.
  
• Familiarity on Data modeling and Data warehousing concepts
  
• Experience with orchestration tools (e.g., Airflow, ADF, Luigi)
  
• Familiarity with at least one cloud platform (AWS, Azure, or GCP)
  
• Strong analytical, problem-solving, and communication skills
  
• Ability to work both independently and as part of a collaborative team
  

Preferred Qualifications

• Experience with DBT (Data Build Tool) for data transformations
  
• Exposure to real-time/streaming platforms (Kafka, Spark Streaming, Flink)
  
• Familiarity with CI/CD and version control (Git) in data engineering projects
  
• Exposure to the e-commerce or customer data domain
  
• Understands the technology landscape, up to date on current technology trends and new technology, brings new ideas to the team