Data Engineer

Strong SQL Foundations: Solid experience writing and optimizing SQL for commercial-scale products (e.g., handling millions of rows and complex joins efficiently).

Pipeline Orchestration: Proven experience using tools like Airflow, dbt, or AWS Glue to manage and monitor production-grade data workflows.

Python Proficiency: Strong Python skills for data transformation, scripting and interacting with various data sources.

ML Engineering Exposure: Practical experience building the data infrastructure that supports machine learning, including data preprocessing and model deployment pipelines. Experience with machine learning models development

Cloud Experience: Hands-on experience with cloud data platforms, with a strong preference for GCP .

Software Best Practices: Familiarity with Git, CI/CD, and basic containerization (Docker) to ensure code quality and deployment reliability.

Problem-Solving Mindset: A practical approach to engineering that balances the need for speed with long-term system stability.

Bonus Points

• Experience with event streaming (e.g., Kafka, Kinesis ) for real-time data needs.

• Exposure to ML platforms and tools such as SageMaker, Vertex AI, or Databricks .

• Familiarity with BI and visualization tools like Looker or Tableau .

• An interest in eCommerce dynamics and customer behavior analytics.