Senior Data Engineer (ML/AI)

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.