Data Engineer – Credit Risk

Job Purpose

The Credit Data Engineer is responsible for building, maintaining, and optimizing the data infrastructure required for credit risk modelling, analytics, and portfolio monitoring. The role ensures data availability, quality, and readiness for use by risk analysts, modellers, and decision engines across lending products in a bank, microfinance bank, or digital lending organization.

Key Responsibilities

1. Data Pipeline Development & ETL Automation • Design, build, and maintain scalable ETL/ELT pipelines to extract, transform, and load credit data from core banking systems, LOS, LMS, collections platforms, and credit bureaus. • Automate recurring data ingestion processes for modelling, reporting, and risk analytics. • Optimize data flows to ensure high performance and reliability.

2. Data Architecture & Storage • Develop and maintain credit data warehouses, data lakes, and modelling datasets. • Create structured datasets for underwriting, scorecards, IFRS 9, and portfolio monitoring. • Ensure proper indexing, partitioning, and schema design for efficiency.

3. Data Quality & Governance • Implement data quality checks, anomaly detection, and reconciliation routines. • Maintain data dictionaries, metadata, and lineage for credit systems. • Work closely with IT and Risk teams to enforce data governance standards.

4. Support for Modelling & Analytics • Provide clean and curated datasets to credit modellers, analysts, and data scientists. • Build feature stores and modelling-ready datasets for PD, LGD, EAD, and scorecards. • Support development of data pipelines for machine learning and real-time decision engines.

5. System Integration & API Development • Integrate with external data providers (credit bureaus, ID verification APIs, AML/KYC databases). • Support deployment of scoring and decisioning models into production systems. • Build and maintain APIs for real-time credit checks, scoring, and risk assessments.

6. Operational Reporting Support • Build data structures supporting dashboards (Power BI, Tableau, Qlik). • Ensure daily/weekly/monthly portfolio reporting datasets are automated and accurate. • Support auditors and regulators with required data extracts.

7. Collaboration & Cross-Functional Support • Work closely with Credit Risk, Modelling, Collections, Fraud, and Product teams. • Provide technical guidance on data issues affecting loan approvals, disbursements, and collections. • Support digital lending, scorecards, and credit decision engine enhancements.

Qualifications & Experience • Bachelor’s degree in Computer Science, Data Engineering, Information Systems, Mathematics, or related field. • 5 –7+ years of experience in data engineering, preferably in financial services. • Strong skills in SQL, Python, or Scala. • Experience with ETL tools (Airflow, DBT, Talend, NiFi, etc.). • Knowledge of cloud platforms (AWS, GCP, Azure) is an advantage. • Understanding of credit risk data (delinquency, scorecards, ECL, bureau data, etc.). • Experience with core banking systems, LOS/LMS, or fintech systems is a plus. • Build feature stores and modelling-ready datasets for PD, LGD, EAD, and scorecards. • Support development of data pipelines for machine learning and real-time decision engines.

Skills & Competencies • Strong SQL and database engineering expertise. • Ability to work with large datasets efficiently. • Knowledge of credit risk data flows and lending systems. • Familiarity with APIs, real-time streaming (Kafka), or microservices. • Excellent problem-solving and debugging skills. • Attention to detail and strong documentation habits. • Ability to work cross-functionally in fast-paced lending environments.

Job Type: Full-time

Experience: • Strong SQL & Database Engineering: 6 years (Required) • Data Pipeline & ETL Development: 5 years (Required) • Python for Data Engineering: 5 years (Required) • Understanding of Credit Risk Data: 5 years (Required) • API & System Integration: 5 years (Required)

Location: • Lagos (Required)