Data Engineer

At Q, we are committed to delivering exceptional retail experiences and innovative solutions to our customers. Data is the foundation of everything we do, empowering us to make smarter decisions and serve our customers better. We are looking for an exceptional Data Engineer to join our team and help build the robust data infrastructure that powers our continued growth.

In this role, you will be the architect of our data ecosystem. You will break down data silos within our enterprise ecosystem, ensuring that our Machine Learning Engineers and Data Analysts have access to clean, reliable, and high‑velocity data. If you view data as a strategic asset rather than just a storage challenge, we want you on our team.

• Architect & Build:
  Design, construct, and maintain highly scalable and robust ETL/ELT data pipelines from the ground up.
• Unify Data:
  Extract and harmonize complex data structures from Microsoft Dynamics and SQL Server into a centralized, analytics‑ready single source of truth.
• Ensure Quality:
  Implement rigorous data quality checks, cleansing processes, and automated alerting to ensure our AI models are fed with trusted, validated, and high‑quality data.
• Collaborate:
  Work side‑by‑side with AI Product Managers, ML Engineers, and Business Analysts to transform raw data into actionable fuel for predictive models such as dynamic pricing engines, and AI solutions aimed at optimizing conversion rates, average basket size, and user retention.
• Govern & Secure:
  Champion data governance, ensuring all data infrastructure complies with privacy regulations and security best practices.

• 3-5 years of proven experience as a Data Engineer, Data Architect, or similar role building production‑grade data infrastructure.
• Advanced proficiency in SQL and Python data manipulation and pipeline development.
• Deep expertise in the Microsoft data ecosystem, specifically Azure Synapse Analytics, Azure Data Factory (ADF), and SQL Server.
• Strong expertise in building and orchestrating data pipelines using tools like Apache Airflow, dbt, or similar technologies.
• Experience with big data processing frameworks (e.g., Apache Spark, Kafka).
• A problem‑solving mindset with a relentless focus on performance optimization and system reliability (Uptime & Low Latency).

• Previous experience working with retail, e‑commerce, or supply chain data.
• Previous experience extracting and transforming data from Microsoft Dynamics (e.g., using Azure Synapse Link or custom API connectors) into an analytics database.
• Familiarity with Machine Learning life cycles and MLOps tools.
• Knowledge of regional data privacy frameworks (e.g., PDPL).