Data Engineer
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Key skills for this role
About the Role
Unlock the power of data as a Data Engineer at Q, where we prioritize exceptional retail experiences and innovative solutions. This role is pivotal in architecting our data ecosystem, breaking down silos to ensure seamless access to clean and reliable data for our Machine Learning Engineers and Data Analysts.
Key Skills for This Role
Full Job Posting
About The Role
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.
Qualifications
- 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).
- Nice to Have
- 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 lifecycles and MLOps tools.
- Knowledge of regional data privacy frameworks (e.g., PDPL).
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