Data Engineer

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 #J-18808-Ljbffr