Credit Risk Data Engineer

Description

The company serves over 1.5 million customers around the world with tailored payment solutions, from transaction processing and customer service to new product launches. It is a wholly owned subsidiary of a larger bank.

The Credit Risk Data Strategy team, within Credit Risk Management, is hiring a Data Engineering Analyst to help modernize credit risk data over a multi-year program. You will work with Data Strategy colleagues, Technology and business stakeholders on data products, source-system onboarding and migrations from legacy and SAS-based platforms to a cloud architecture built on Databricks, Snowflake, SQL and Python.

The aim is trusted, governed and scalable data for regulatory, forecasting, modeling and reporting work, with better automation, data quality and efficiency.

Responsibilities

• Work with Data Strategy colleagues, business stakeholders, the Enterprise Data Office and Technology teams on credit risk modernization and data product work.
• Help build portfolio and cross-portfolio Credit Risk data products that are trusted, governed and matched to business needs.
• Design, build, test and maintain scalable data solutions in SQL, Python, Databricks and other modern tools.
• Work with Technology to onboard source-system data, document data requirements and support future-state credit risk architectures.
• Build and improve transformation, validation, reconciliation and automation processes for efficiency, scale and quality.
• Move legacy SAS-based and SQL-based credit risk processes to Python, SQL and cloud platforms.
• Write reusable code, pipelines and automation that follow established development standards and version control.
• Profile and analyze large, complex datasets to find issues, trends and areas for improvement.
• Support governance work on data quality, lineage, controls, testing and documentation.
• Write and maintain technical documentation, process flows, playbooks and training materials.
• Help troubleshoot, find root causes for and fix data and processing problems.
• Look for ways to use automation, AI and machine learning to simplify processes, strengthen quality checks and support decisions.

Requirements

• A bachelor's degree in information systems, computer science, mathematics, statistics, engineering or a related field, or equivalent work experience.
• 6+ years in data engineering, data science, data analytics or related technology roles.
• Strong hands-on skills in SQL and Python programming.
• Experience with large-scale datasets, including building transformations, pipelines, validation routines and reconciliations.
• Experience with Databricks, Snowflake, Azure, AWS or another cloud-based data platform.
• Experience supporting data modernization, migration, cloud transformation or data platform implementation.
• Understanding of data architecture, database design, data modeling and data management.
• Strong analytical, problem-solving and troubleshooting skills.
• Ability to turn business and data requirements into solutions that are easy to maintain.
• Strong communication and collaboration skills across business, data and technology teams.
• Preferred: experience in credit risk, regulatory reporting, banking or financial services.
• Preferred: experience with data quality, reconciliation, controls and testing frameworks.
• Preferred: experience building reusable Python frameworks, automation solutions and data pipelines.
• Preferred: experience with Git-based version control, CI/CD pipelines and deployment for data applications and products.
• Preferred: experience with Agile delivery and tools such as Jira or Azure DevOps.
• Preferred: familiarity with Power BI or other data visualization and reporting tools.
• Preferred: experience migrating SAS-based processes to SQL, Python or cloud technologies.

Conditions

You must work from a company location 3 or more days per week.

The posting may close early because of high application volume, so applying early is encouraged.

The company gives timely updates throughout the hiring process.

Pay is fair, equitable and transparent, in line with local laws, with equal pay for work of equal value.

If you need adjustments during hiring, ask your recruiter for support.

Benefits

• Market-competitive pay within defined salary ranges tied to industry benchmarks and internal equity.
• Performance-based incentives for eligible employees, under plan rules, based on transparent, objective criteria for individual and company results.
• Benefits open to all employees, focused on financial wellbeing and health and wellness.
• Training, education support and career progression paths based on transparent criteria.
• Recognition programs that celebrate achievements and milestones.
• Compensation and benefits are reviewed regularly, and full package details are shared on application.