Senior Data Engineer (ML/AI) at Sana Commerce Latinoamrica

Alexandria, Alexandria, Egypt -

Full Time

Start Date

Immediate

Expiry Date

19 May, 26

Salary

0.0

Posted On

18 Feb, 26

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Engineering, Machine Learning, Artificial Intelligence, Data Pipelines, Feature Engineering, MLOps, Spark, Databricks, Azure Synapse, SQL, Python, Airflow, Prefect, Azure Data Factory, Data Quality, PySpark

Industry

Software Development

Description

Company Description What started in 2007 with a pizza and a plan has grown into a fast-moving SaaS company empowering manufacturers, distributors, and wholesalers to thrive in complex B2B commerce. Our mission is simple: help businesses build stronger relationships through seamless digital commerce. At Sana Commerce, we're looking for a Data Engineer (ML/AI) to design, build, and scale data systems that power our analytics and machine learning initiatives. Your work will ensure high-quality, reliable, and ML-ready data pipelines that enable both traditional analytics and advanced AI-driven solutions across the business. Job Description 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. Qualifications What you bring 8+ 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. 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. Additional Information #LI-Hybrid

Responsibilities

The role involves designing and maintaining scalable data systems and pipelines optimized for Machine Learning and AI workloads, including building feature stores for consistent data use by models. This includes collaborating with Data Scientists and supporting MLOps practices for model deployment and monitoring.