• Accountability: Data Pipeline & Integration – 30% • Design and implement automated ETL (Extract, Transform, Load) pipelines to collect data from core banking systems, mobile apps, ATMs, and third-party APIs. • Standardize and transform raw data into consistent formats for downstream systems. • Ensure secure, encrypted data transfer and enforce access controls to protect sensitive financial information. • Contribute to the data architecture by defining how data flows across systems, ensuring scalability, modularity, and maintainability. • Accountability: Data Warehousing & Management – 25% • Build and manage data warehouses and data lakes to store structured and unstructured data efficiently. • Apply data modeling techniques and optimize storage using indexing, partitioning, and compression. • Implement data lifecycle management, including retention, archival, and deletion policies. • Set up data backup and replication strategies to ensure high availability, disaster recovery, and business continuity. • Align storage solutions with the bank’s enterprise data architecture, ensuring compatibility with analytics, reporting, and compliance systems. • Accountability: Compliance & Real-Time Processing – 25% • Automate data preparation for regulatory reporting (e.g., KYC, AML, Basel III) using governed ETL workflows. • Build real-time data processing systems using tools like Apache Kafka or Spark Streaming for fraud detection and transaction monitoring. • Ensure data lineage, auditability, and traceability to support compliance audits and internal controls. • Design real-time processing components as part of the broader data architecture, ensuring they integrate seamlessly with batch systems and reporting tools. • Accountability: Collaboration, Data Quality & Governance – 20% • Work with data scientists and analysts to deliver clean, reliable datasets for modeling and reporting. • Apply validation rules, anomaly detection, and monitoring to maintain high data quality across ETL pipelines. • Maintain metadata catalogs, data dictionaries, and lineage tracking to support transparency and governance. • Collaborate with data stewards and architects to enforce data governance policies and ensure alignment with the bank’s overall data strategy.

• Bachelor’s degree in Computer Science, Software Engineering, Information Technology, Data Science, Computer Engineering, Mathematics, Statistics, or a related field. (Master is an added advantage) • Relevant professional certifications in data engineer like, Google Cloud Data Engineer, Azure Data Engineer (DP-203), AWS Data Analytics Specialty, Databricks Data Engineer, Snowflake, Kafka, Kubernetes, Analytics, Machine Learning, Artificial Intelligence and Cloud Platforms (GCP, AWS, Azure) are considered added advantages • Preferred Experience •At least 3-5 years’ experience in working on building data pipelines, working with big data and cloud platform, managing real-time and warehouse data systems, and collaborating with cross-functional teams. • Financial domain knowledge is an added advantage • Knowledge and Skills • Technical Proficiency: Skilled in data modeling, ETL/ELT, big data tools, programming (Python, R, SQL), data visualization, and cloud platforms. • Analytical & Problem-Solving: Able to manage complex datasets, optimize pipelines, and ensure data quality. • Communication & Collaboration: Effective in documenting workflows and working with cross-functional teams. • Education • Bachelor’s Degree: Information Technology (Required)