Would you like to use your data engineering skills to revolutionize retail? Be part of the development and improvement of a dynamic pricing B2B SaaS platform, leading the charge in automating and optimizing pricing strategies with AI-driven insights.

It is a comprehensive solution, embraced by retailers and brands across over 40 markets, that delivers actionable pricing recommendations that drive business growth and profitability.

This is an invitation to join a dynamic and pioneering team where your expertise in data will significantly influence the path forward. Our client's team is passionate about tackling real-world challenges and seeks like-minded individuals eager to make a mark in the retail pricing arena.

Required skills:

A Bachelor’s or higher in Computer Science, Software Engineering, or a closely related technical field, demonstrating strong analytical and coding skills.
3+ years of experience as a data engineer, software engineer, or similar role and using data to drive business results.
Strong Python skills, with experience building modular, testable, and production-ready code.
Solid understanding of databases such as SQL, including indexing best practices, and hands-on experience working with large-scale data systems (e.g., Spark, Glue, Athena).
Practical experience with Airflow or similar orchestration frameworks, including designing, scheduling, maintaining, troubleshooting, and optimizing data workflows (DAGs).
A solid understanding of data engineering principles: ETL/ELT design, data integrity, schema evolution, and performance optimization.
Familiarity with AWS cloud services, including S3, Lambda, Glue, RDS, and API Gateway.

Nice-to-Haves:

Experience with MLOps practices such as CI/CD, model and data versioning, observability, and deployment.
Familiarity with API development frameworks (e.g., FastAPI).
Knowledge of data validation techniques and tools (e.g., Great Expectations, data drift detection).
Exposure to AI/ML system design, including pipelines, model evaluation metrics, and production deployment.

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Scope of work:

As a Data Engineer you’ll have end-to-end ownership - from system architecture and software development to operational excellence. Specifically, you will:

‍Design and implement scalable machine learning pipelines with Airflow, enabling efficient parallel execution.
Enhance our data infrastructure by refining database schemas, developing and improving APIs for internal systems, overseeing schema migrations, managing data lifecycles, optimizing query performance, and maintaining large-scale data pipelines.
Implement monitoring and observability, using AWS Athena and QuickSight to track performance, model accuracy, operational KPIs, and alerts.
Build and maintain data validation pipelines to ensure incoming data quality and proactively detect anomalies or drift.
Collaborate closely with software architects, DevOps engineers, and product teams to deliver resilient, scalable, production-grade machine learning pipelines.