Big Data Analytics & Machine Learning Engineer

EXL is a global analytics and digital solutions company that enhances business outcomes through data management and innovative solutions. The role of Big Data Analytics & Machine Learning Engineer involves designing and maintaining scalable data pipelines, collaborating with data scientists to implement machine learning models, and optimizing data management processes for enterprise datasets.

Responsibilities:

• Design, build, and maintain scalable data pipelines to process and transform large-scale enterprise datasets
• Collaborate closely with Data Scientists to operationalize basic ML models, ensuring they perform efficiently in a distributed computing environment
• Utilize Hive for data warehousing and management, ensuring high performance for complex analytical queries
• Write complex, optimized SQL and PySpark code to extract insights and prepare features for modeling
• Optimize Spark jobs and SQL queries to handle enterprise scale data sets with minimal latency
• Serve as the technical bridge between data engineering infrastructure and the data science lifecycle

Requirements:

• 5–8 years of professional experience in Big Data Engineering or ML Engineering
• Expert-level proficiency in PySpark
• Strong SQL skills for deep-dive analysis and extensive experience working with Hive databases
• Advanced Python skills, specifically for data science libraries (Pandas, Scikit-learn, NumPy)
• Previous experience working as an ML Engineer, including model deployment, monitoring, and feature engineering
• Proven track record of handling 'messy' enterprise data at scale
• Familiarity with CI/CD pipelines for ML (MLOps)
• Previous experience in Banking/Capital Markets/Financial Services is required