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.

• 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 Sage Maker, 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.

• 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
• Sage Maker
• 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.

• Experience building recommendation systems or personalization engines.
• Familiarity with ML frameworks such as Scikit‑Learn, XGBoost, Tensor Flow, 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.

Python | AWS (S3, Glue, Sage Maker, ECS/Fargate) | Snowflake | SQL | MLOps | Machine Learning | Feature Engineering | Recommendation Systems | Airflow | Docker | CI/CD