Senior Software Architect - Platform

Fiserv•Alpharetta, GA

15h•$145,000 - $240,000•Onsite

About The Position

Data Commerce Solutions (DCS), a Fiserv business unit, turns the company’s data into scalable platforms and products across credit, marketing, verification, fraud, and data insights. The Senior Software Architect is the hands-on technical owner of reusable platform capabilities that support multiple DCS product verticals, combining architecture with direct software engineering. The role focuses on creating secure, scalable, supportable platform services and enabling data science proofs of concept to become versionable, enterprise-scale production code. This is an on-site role Monday through Friday, with approximately 10% travel off-site or to other office locations. Candidates must currently possess valid and unrestricted U.S. work authorization; sponsorship is not available now or in the future. The salary range is $145,000 to $240,000 for employees in New Jersey and New York; ranges may differ in other states. Incentive-eligible associates may receive an annual incentive opportunity that may be delivered as cash bonus and equity awards at the Company’s discretion.

Requirements

10+ years of progressive software and data engineering experience, including significant responsibility for architecture and hands-on delivery of distributed systems.
Advanced coding proficiency in Java and strong working knowledge of Python and data science development patterns.
Demonstrated experience converting notebooks, experimental pipelines, models, or analytical prototypes into versioned, tested, maintainable production software.
Deep experience with Spark, microservices, REST or event-driven APIs, SQL and NoSQL systems, containerization, Kubernetes, and cloud architecture; Microsoft Azure is preferred.
Experience designing CI/CD and MLOps or DataOps workflows, including artifact repositories, automated testing, model or feature versioning, observability, and controlled promotion across environments.
Strong understanding of distributed systems, performance engineering, data formats such as Iceberg, Delta Lake, Avro, and Parquet, and Lakehouse, batch, and streaming architectures.
Hands-on knowledge of Git, Maven or Gradle, automated testing, Agile, and DevOps practices.
Ability to mentor early-career engineers while maintaining high engineering standards and to communicate complex architecture through concise documentation, diagrams, and code.
Bachelor’s degree in computer science, engineering, or a related field, or an equivalent combination of education and relevant experience.
Valid and unrestricted U.S. work authorization; candidates requiring sponsorship now or in the future will not be considered.

Nice To Haves

Experience building internal developer platforms, model or feature platforms, code transformation frameworks, or self-service engineering capabilities.
Experience with domain-driven design, test-driven development, infrastructure as code, SRE practices, and high-volume transaction processing.
Experience in financial services, banking, payments, credit, marketing, verification, fraud, or data insights.
Master’s degree in computer science, engineering, data science, or a related discipline.

Responsibilities

Own the architecture, design, coding, testing, and production implementation of shared data and software platform capabilities.
Create engineering frameworks and automation to convert data science proofs of concept—including notebooks, Python code, features, models, and validation logic—into modular, testable, version-controlled, deployable software.
Build CI/CD workflows that support validation, promotion, observability, rollback, and lineage.
Re-engineer proof-of-concept workloads for enterprise scale, including distributed processing, low-latency serving, batch throughput, fault tolerance, and cost optimization.
Build reference implementations and production code using technologies such as Java, Python, Spark, Spring Boot, APIs, microservices, containers, Kubernetes, and cloud-native data services.
Architect reusable, secure platform services for data ingestion, feature engineering, model execution, workflow orchestration, metadata, policy enforcement, and monitoring, with built-in security and privacy controls.
Establish coding standards, reusable libraries, templates, test harnesses, and paved-road deployment patterns.
Partner with data scientists, product, security, DevOps, and SRE teams to define production-readiness criteria.
Lead architecture decisions through working code, prototypes, design documentation, and performance tests.
Participate in production issue resolution and evaluate emerging technologies to improve reliability, scalability, and engineering velocity.
Mentor early-career engineers and communicate complex architecture through concise documentation, diagrams, and code.