Data & ML Engineer

Fiserv•Alpharetta, GA

1d•Onsite

About The Position

As a Data & ML Engineer, you will help build and support the data engineering, ETL, and MLOps capabilities that power Merchant Opportunity Analysis (MOA) and Offer Engine within the Digital Onboarding team. Merchant Opportunity Analysis (MOA) refers to the analytical capability used to identify merchant needs, growth opportunities, product fit, and offer recommendations that can improve onboarding, personalization, and customer acquisition outcomes within Digital Onboarding. This role will focus on developing data pipelines, supporting feature preparation, maintaining ETL workflows, and helping operationalize machine learning outputs used for customer insights, personalization, and offer optimization. You will work closely with senior engineers, data scientists, backend engineers, and product partners to deliver reliable data and ML capabilities that support Digital Onboarding experiences.

Requirements

4+ years of experience in data engineering, ETL development, ML engineering, analytics engineering, or software engineering.
Hands-on experience with Python, SQL, and data pipeline development.
Experience working with data integration or ETL tools such as AWS Glue, Qlik Data Integration, Snowflake, or similar platforms.
Familiarity with AWS services such as S3, Glue, Lambda, SageMaker, CloudWatch, or related cloud services.
Understanding of data modeling, data quality, orchestration, metadata, and pipeline monitoring.
Exposure to machine learning workflows, feature engineering, batch scoring, or model deployment support.
Experience working with structured and semi-structured data.
Familiarity with Git, CI/CD pipelines, automated testing, and Agile delivery practices.
Strong problem-solving skills and ability to troubleshoot pipeline and data issues.
Ability to collaborate effectively with engineering, data science, product, and business teams.
Bachelor’s degree in Computer Science, Information Technology, Information Systems, or a related field (or equivalent industry experience).

Nice To Haves

Experience supporting offer optimization, personalization, recommendation, customer insights, onboarding, or digital acquisition platforms.
Exposure to MLOps practices, model registries, feature stores, batch scoring, or model monitoring.
Experience with CDC, event-driven integration, or near-real-time data movement.
Experience working with merchant, customer, application, product, or transaction data.
Experience in financial services, fintech, merchant services, payments, or digital commerce.
Experience using AI-assisted development tools or Agentic SDLC practices.
AWS Data Analytics, Cloud Practitioner, or related certifications.

Responsibilities

Build and maintain data pipelines that support MOA, Offer Engine, customer insights, personalization, and Digital Onboarding use cases.
Develop ETL workflows using Python, SQL, AWS Glue, Qlik Data Integration, Snowflake, and related tools.
Support ingestion, transformation, validation, and delivery of internal and external data sources.
Assist with feature preparation, scoring workflows, model output processing, and ML integration patterns.
Support MLOps activities including deployment workflows, monitoring, model output validation, and operational support.
Implement data quality checks, error handling, reconciliation logic, and pipeline monitoring.
Work with data scientists to understand feature needs and help prepare datasets for modeling and production use.
Collaborate with backend engineers to support integration of model outputs, recommendation data, and customer insights into Digital Onboarding applications.
Troubleshoot data pipeline issues, production defects, and integration failures.
Follow engineering standards for code quality, CI/CD, documentation, security, and operational readiness.