Sr. ML Data Engineer (ETL

at  CEDENT

QUALIFICATIONS:

• 7 years in data engineering and at least 4 years focusing on ML feature engineering ETL pipeline development and data preparation for ML
• Proven experience managing pipelines on Data bricks using Apache Spark with a strong understanding of the medallion architecture
• Familiarity with ML lifecycle management with MLflow experience as a strong plus and advanced skills in Apache Spark PySpark for big data processing and analytics
• Proficient in Python for data manipulation and SQL for query optimization
• Experience building pipelines for real-time and batch model serving in production environments and knowledge of CICD practices for ETLELT pipeline development
• Expertise in metadata and master data management within technical data catalogues
• Understanding of data security and compliance especially with sensitive data like PII
  Mandatory Skills: : Apache Spark, Databricks, Java, Python, Scala, SparkSQ

• Feature Engineering Data Integration Develop and maintain feature engineering pipelines using Data bricks to support ML models effectively
• Data Pipeline Development Integrate diverse data sources eg clickstreams user behavior demographic data to create user behavior features profiles for complex ML tasks
• Medallion Architecture Design and implement ETL, ELT pipelines aligned with the bronze silver and gold layers of the medallion architecture
• Model Support Build data pipelines to support ML model training calibration and deployment leveraging MLflow for experiment tracking and performance monitoring
• Query Optimization Low Latency Pipelines Design low latency production ready data pipelines to support real-time and batch model inference
• CICD Practices Apply CICD principles for seamless pipeline deployment
• Data Governance Ensure pipelines comply with security and regulatory standards particularly for handling PII and maintain metadata and master data across the data catalogue
• Collaboration Work closely with ml scientists ml engineers and other stakeholders to align data transformation with business objectives