Data Engineer

Job Summary Fiserv is seeking a highly skilled Data Engineer with strong AI/ML and MLOps experience to join a team building next-generation recommendation systems and advanced analytics platforms using large-scale merchant datasets. This role requires a hybrid engineer who can work across data engineering, machine learning integration, feature engineering, and cloud-native deployment pipelines. The ideal candidate will have hands-on experience building scalable data platforms, developing ML-ready datasets, deploying machine learning workflows, and supporting model lifecycle management within an AWS ecosystem. Key Responsibilities • Design, develop, and maintain scalable data pipelines and data models for large-scale merchant datasets. • Build and optimize feature engineering pipelines for machine learning applications. • Create and manage analytical datasets to support recommendation engines and predictive analytics. • Develop and integrate machine learning models into production environments. • Support end-to-end ML workflows including data preparation, model training, evaluation, deployment, and monitoring. • Build and maintain MLOps pipelines for model lifecycle management and automated deployments. • Collaborate with Data Scientists, ML Engineers, and Product teams to deliver data-driven solutions. • Implement inference pipelines using AWS SageMaker, ECS, and other AWS services. • Optimize data processing workflows using Snowflake and cloud-native technologies. • Participate in architecture discussions and contribute to best practices for scalable data and ML platforms. • Support recommendation system development using techniques such as nearest neighbor algorithms, collaborative filtering, and ML-based recommendation models. Required Skills • 7+ years of experience in Data Engineering. • Strong hands-on experience with Python for data engineering and machine learning workflows. • Experience building scalable ETL/ELT pipelines and data platforms. • Strong AWS experience including: • S3 • AWS Glue • SageMaker • ECS/Fargate • Lambda (preferred) • Experience with Snowflake and cloud-based data warehousing. • Knowledge of machine learning concepts, feature engineering, and model deployment. • Experience with MLOps practices including CI/CD, model monitoring, and automated retraining workflows. • Strong SQL and data modeling expertise. • Experience working with large-scale structured and semi-structured datasets. Preferred Qualifications • Experience building recommendation systems or personalization engines. • Familiarity with ML frameworks such as Scikit-Learn, XGBoost, TensorFlow, or PyTorch. • Experience with orchestration tools such as Airflow. • Knowledge of containerization technologies including Docker and Kubernetes. • Experience with Agentic AI, Generative AI, or LLM-based applications (Nice to Have). • Experience in financial services, payments, or merchant analytics domains. Key Technologies Python | AWS (S3, Glue, SageMaker, ECS/Fargate) | Snowflake | SQL | MLOps | Machine Learning | Feature Engineering | Recommendation Systems | Airflow | Docker | CI/CD