Credit Data Engineer

Responsibilities

• Design, develop, and maintain data pipelines and transformation models in Snowflake to support credit monitoring, portfolio reviews, and downstream analytics and ML.
• Build and govern the credit data warehouse layer: joins, unions, business rules, slowly-changing dimensions, and curated marts on top of SAP-landed data and external sources.
• Implement a transformation framework (dbt or equivalent) with tests, documentation, and version control to ensure data quality, lineage, and modularity of credit transformations.
• Orchestrate and monitor pipelines so that downstream consumers (analysts, ML models, Power BI) receive reliable, on-time data - with proper scheduling, alerting, and error handling in place.
• Onboard new data sources - credit insurance feeds, financial information providers, local insolvency registries, and the customer portal - into the credit data platform.
• Drive a data quality framework: freshness, uniqueness, referential integrity, and anomaly detection on critical credit datasets.
• Collaborate with global and regional credit stakeholders, the Credit Data Scientist, and the Credit Data Analyst to translate business and policy requirements into well-modeled data products.
• Support the productionisation of credit risk models, scoring approaches, and early warning indicators by providing the upstream features, pipelines, and orchestration they depend on.
• Demonstrate autonomy in technical execution and delivery of engineering tasks and projects, while aligning with strategic direction from Credit leadership and contributing to a collaborative team environment.
• Knowledge, Skills and Experience:
• Required:
• 3 to 5 years of relevant work experience in data engineering, analytics engineering, or a similar role.
• Bachelor's Degree in Computer Science, Engineering, Big Data, or related field required.
• Strong proficiency in SQL (advanced - window functions, CTEs, query optimization).
• Solid working knowledge of Python for data engineering tasks (pipelines, scripting, data quality).
• Hands-on experience with a cloud data warehouse - Snowflake strongly preferred.
• Working understanding of dimensional data modeling and ETL/ELT patterns.
• Hands-on experience working with ERP-sourced financial data (SAP preferred).
• Experience with Git and standard collaborative development practices (branching, code review, versioning).
• Strong analytical mindset, attention to detail, and problem-solving skills.
• Excellent communication skills in English; additional languages are an advantage.
• Ability to work effectively in a global, multicultural environment.
• Comfortable working independently, managing multiple priorities, and translating business needs into engineering outcomes.
• Preferred / a plus:
• Master's / Postgraduate Degree in Computer Science, Data Engineering, Big Data, or related field.
• Hands-on experience with dbt (or another transformation framework such as Dataform, SQLMesh).
• Experience with an orchestrator such as Airflow, Prefect, Dagster, or Snowflake Tasks.
• Experience with Databricks and/or PySpark.
• Familiarity with CI/CD pipelines for data workflows (GitHub Actions, Azure DevOps, or similar).
• Experience integrating data via REST APIs and working with multiple heterogeneous data sources.
• Exposure to AI/ML applications in credit analytics, forecasting, or risk modeling.
• Familiarity with Power BI, SharePoint, Power Automate, or VBA.

Benefits

Additional Information

Job Purpose: As a Credit Data Engineer within the Credit function, you will design and own the data infrastructure that powers credit risk management, exposure monitoring, and decision-making across customer, account, and transactional data. You will work closely with the Credit Data Scientist, Credit Management leadership, and regional credit teams to build reliable, well-modeled datasets that enable advanced analytics, ML models, and management reporting. This role plays a key part in strengthening TD SYNNEX's credit governance by delivering the data foundation that supports informed credit decisions, proactive risk identification, and continuous improvement of credit processes across regions.

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