Description

Build and optimize large-scale data processing pipelines and scalable data platforms to support machine learning products.

You will own the full lifecycle of data engineering for ML initiatives, from ingestion to model integration.

Responsibilities

• Design, build, and maintain scalable ETL/ELT workflows and data pipelines.
• Partner with Data Science and ML Engineering teams to prepare and serve high-quality datasets for modeling and inference.
• Process structured and unstructured data at scale using big data technologies.
• Develop and optimize data architectures for performance, reliability, and security.
• Automate data workflows and support CI/CD for both data and ML pipelines.

Required Skills

• 10+ years of professional experience in Data Engineering.
• Strong programming skills in Python, Scala, or Java.
• Hands-on experience with Apache Spark, Hadoop, Kafka, or similar big data technologies.
• Solid experience in SQL and relational database systems.
• Experience supporting ML workflows, including data preparation, feature engineering, and model integration.
• Familiarity with cloud platforms such as GCP, AWS, or Azure.
• Working knowledge of CI/CD, Git, and infrastructure-as-code tools.
• Strong understanding of data warehousing concepts and tools like BigQuery, Snowflake, or Redshift.

Preferred Skills

• Experience with MLflow, Kubeflow, or similar ML orchestration tools.
• Familiarity with containerization and orchestration using Docker or Kubernetes.
• Prior experience in retail or eCommerce domains.

Key Skills

Education

Any Gradute