Data Engineer

Job Description

As a Data Engineer you’ll design and run the data and ML pipelines that let teams across Marketing, Purchasing, Logistics and Finance make confident, data-driven decisions.

What you’ll do:

• Scalable Pipeline Development: Design and maintain ETL/ELT pipelines capable of handling 10M+ daily events and large-scale data transfers across our platforms.
• Data Modeling: Develop and optimize data models in environments like BigQuery or Snowflake to ensure high performance for both analytics and ML training sets with optimal cost
• Support ML Workflows: Build the underlying features and data inputs required for Machine Learning models
• Develop and refine ML models for practical business use cases, such as customer sentiment, churn prediction or demand forecasting
• MLOps Integration: Establish and maintain MLOps pipelines to help automate the deployment and monitoring of models in production.
• System Integration: Work with internal APIs and third-party tools to ingest data efficiently while maintaining strict data integrity.
• Governance & Quality: Implement best practices for data quality, security, and documentation to ensure our data remains a “source of truth.”
• Development according to software engineering best practices (Git, CI/CD, trunk based development, tests)
• AI Collaboration: Contribute to experiments with AI and LLMs to assess how they can be practically applied to solve business problems.
  
  

What you’ll need:

• 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.

Are you interested in this position?
Apply by clicking on the “Apply Now” button below!
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