What you'll be doing

Designing and maintaining data pipelines optimized for ML/AI workloads , including handling of large-scale, unstructured, and semi-structured data.

Building feature pipelines and feature stores that ensure reusability and consistency of data used by machine learning models.

Collaborating with Data Scientists and ML Engineers to understand data requirements for training, validation, and production deployment .

Ensuring data quality, lineage, and governance meet standards required for AI/ML applications.

Supporting MLOps practices by integrating data pipelines with model training, monitoring, and deployment workflows.

Leveraging distributed processing frameworks (e.g., Spark, Databricks, Azure Synapse) for scalable ML data processing .

Why you ll love working here

Impact from day one

Join a scale-up where your ideas shape how global businesses operate online.

Continuous learning

Access a structured onboarding rated 9.1/10 by previous hires, mentorship, and feedback culture.

Hybrid flexibility

Work from our office 3 days per week and from home 2 days.

Career growth

Expand your technical and leadership scope in a company built for long-term success.

Our values

At Sana Commerce, our values drive everything we do:

Champions of Our League

We deliver lasting success, balancing quick wins and long-term value

Supercharge Our Customers

We re revolutionizing B2B commerce together, helping our customers to lead and succeed.

Determined to Grow

We embrace challenges, growing and raising the bar for ourselves and our industry.

Bold Together

We dare to be bold because we have each other s back.

Ready to build reliability that scales? Apply now and help shape the foundation of our next-generation SaaS platform.

Desired Candidate Profile

What you bring

5+ years of experience as a Data Engineer, working with Azure and Databricks, ideally with exposure to ML/AI-related data workflows .

College degree that demonstrates your analytic abilities, such as Econometrics, Computer Sciences, Mathematics or similar;

Excellent analytical and problem-solving skills;

Experience with data preparation for ML/AI : managing large datasets, feature engineering, and real-time or batch data pipelines.

Familiarity with MLOps concepts and how data engineering supports model lifecycle management.

Experience with orchestration frameworks (Airflow, Prefect, or Azure Data Factory) for complex ML pipelines .

Knowledge of unstructured data processing (text, images, logs) is a plus.

Strong SQL and Python skills; experience with distributed data processing (PySpark, Dask, etc.) is a plus.