Senior Data & ML Engineer

Calling all innovators – find your future at Fiserv.

We’re Fiserv, a global leader in Fintech and payments, and we move money and information in a way that moves the world. We connect financial institutions, corporations, merchants, and consumers to one another millions of times a day – quickly, reliably, and securely. Any time you swipe your credit card, pay through a mobile app, or withdraw money from the bank, we’re involved.

If you want to make an impact on a global scale, come make a difference at Fiserv.

Job Title

Senior Data & ML Engineer

As a Senior Data & ML Engineer, you will play a lead technical role in building and operationalizing the data engineering, ETL, and MLOps capabilities that support 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 designing scalable data pipelines, production-ready feature workflows, model integration patterns, and reliable data services that enable customer insights, personalization, and offer optimization. You will work closely with data scientists, backend engineers, product teams, and platform partners to ensure data and machine learning capabilities are reliable, observable, secure, and ready for enterprise-scale use.

• Lead the design and implementation of data pipelines supporting MOA, Offer Engine, customer insights, personalization, and Digital Onboarding use cases.
• Build scalable ETL and data integration workflows using Python, SQL, AWS Glue, Qlik Data Integration, Snowflake, and related technologies.
• Design and support feature pipelines, scoring workflows, model output processing, and ML integration patterns for production use cases.
• Partner with data scientists to product ionize machine learning models, analytical features, segmentation outputs, and recommendation logic.
• Establish and mature MLOps practices including model packaging, deployment automation, monitoring, lineage, governance, and retraining support.
• Implement data quality checks, reconciliation processes, observability, exception handling, and operational controls across data pipelines.
• Build batch and near-real-time data movement patterns to support onboarding journeys, merchant matching, and offer recommendation workflows.
• Collaborate with backend engineers to integrate customer insights, recommendation outputs, and offer data into Digital Onboarding APIs and workflows.
• Provide technical leadership through design reviews, code reviews, solution documentation, and mentoring of other engineers.
• Partner with architecture, security, infrastructure, and governance teams to ensure solutions meet enterprise standards.

• 8+ years of experience in data engineering, ML engineering, ETL development, analytics engineering, or platform engineering.
• Strong hands-on experience with Python, SQL, data pipeline development, and production-grade data engineering patterns.
• Experience designing and building ETL pipelines using AWS Glue, Qlik Data Integration, Snowflake, or comparable technologies.
• Experience with AWS services such as S3, Glue, Lambda, Sage Maker, Cloud Watch, Step Functions, ECS/EKS, or similar cloud-native services.
• Experience supporting production ML workflows including feature engineering, batch scoring, model deployment, and model monitoring.
• Strong understanding of data modeling, data quality, partitioning, orchestration, metadata, observability, and lineage.
• Experience building reliable, secure, and scalable data services for customer-facing or business-critical platforms.
• Experience with CI/CD, Dev Sec Ops , version control, automated testing, and release management practices.
• Strong troubleshooting skills across data pipelines, ML workflows, application integrations, and production issues.
• Ability to influence technical direction and collaborate across engineering, data science, product, and business…