Data & Model Operations Engineer

Credigy•Norcross, GA

1d•Onsite

About The Position

Data doesn't govern itself — and at Credigy, we believe the best models are only as good as the pipelines and governance frameworks behind them. The Data & Model Operations team sits at the intersection of data engineering, governance, and model lifecycle integration, building the infrastructure that makes our analytical workflows reliable, auditable, and ready for production at scale. As a Data & Model Operations Engineer, you’ll play a central role in unifying how data moves, transforms, and feeds critical credit risk processes across the organization. If you’re the kind of person who gets energized by solving upstream problems that make everything downstream work better — and you want to do that work in a company where it matters directly to the business — this role is for you. Please note that we are unable to provide visa sponsorship for this role. Candidates must be legally authorized to work in the United States now and in the future without employer sponsorship. You’ll work within Credigy’s Credit – Data Science & Analytics team, partnering closely with deal, analytics, and engineering teams to deliver scalable data and model solutions that hold up under scrutiny. Your work will touch everything from enterprise data governance and pipeline orchestration to model operationalization — the kind of foundational work that enables better decisions across the organization. You’ll be expected to bring both technical depth and the ability to collaborate across teams, translating complex requirements into production-ready workflows. Success here means delivering infrastructure and processes that are not just functional, but governed, repeatable, and built to last.

Requirements

2+ years of relevant full-time experience in data engineering, analytics, data governance, or ModelOps-related roles.
Strong SQL skills and hands-on experience with Snowflake or similar cloud data platforms.
Programming experience in Python, SAS, or R.
Experience working with large datasets, analytical workflows, and pipeline orchestration.
A Bachelor’s degree in a quantitative field such as Computer Science, Data Science, Statistics, Mathematics, or Engineering — or equivalent experience.

Nice To Haves

Experience with credit risk models or model lifecycle processes.
Hands-on experience with machine learning models and familiarity with MLOps concepts, including model training, testing, deployment, and monitoring.
Experience with data governance, lineage, profiling, or catalog tools (e.g., Informatica CDGC).
Exposure to dbt, APIs, CI/CD, Docker, workflow orchestration, or reusable ELT frameworks.
Comfort with AI-assisted development tools such as GPT, Claude, or Informatica Claire AI — or a strong desire to develop that fluency.
A Master’s degree in a quantitative field is a plus.

Responsibilities

Design and implement Informatica Taskflows integrated with ELT processes to unify data and model operations into governed, end-to-end workflows.
Build and maintain data governance capabilities including enterprise glossary management, technical asset association, metadata extraction, data lineage, and automated data profiling and quality checks.
Integrate model execution, scoring, monitoring, and reporting into scalable production workflows.
Support credit risk model lifecycle processes, including data preparation, validation, deployment, and model refresh activities.
Partner with deal, analytics, and engineering teams to deliver auditable, production-ready data and model solutions.
Contribute to AI-driven initiatives that enhance data governance, pipeline operations, and workflow automation.