As a fullstack developer/Data Engineer, you will work with others in the software engineering team. You will be assigned to specific specialities to maintain and improve current products, create new tools and applications and manage data research and applications.
Key Responsibilities
- – Collaborate Across Teams: Work closely with internal stakeholders, product managers, and vendors to design, develop, maintain, and improve data engineering solutions and products. Communicate effectively with cross-functional teams to align on priorities, limitations, and improvements.
- – Data Pipeline Development: Design, build, test, and optimize robust, scalable, and efficient data pipelines for batch and real-time data processing using appropriate technologies and frameworks.
- – Machine Learning and AI Development: Design and develop machine learning models and AI solutions tailored to specific product and business requirements. Conduct research, testing, and model training for predictive analytics, particularly for credit scoring and decision-making algorithms.
- – Data Quality Assurance: Establish and maintain data quality standards. Develop automated data validation, cleansing, and monitoring tools to ensure data accuracy and reliability.
- – Database Architecture Design: Design, develop, and maintain data architectures, including relational databases, NoSQL databases, data lakes, and data warehouses, ensuring data integrity, security, and scalability.
- – Data Integration: Lead the integration of third-party data sources, APIs, and custom solutions into the existing data infrastructure. Ensure seamless data flow across different systems and applications.
- – Technical Documentation: Develop comprehensive technical specifications, architecture documentation, and user guides. Ensure that all documentation is timely, accurate, and maintained up to date.
- – Work closely with product managers to receive information on limitations and improvements while clearly and regularly communicating to support colleagues.
- – Independently install, customise and integrate third party solutions.
- – Problem Solving and Troubleshooting: Proactively identify and resolve complex data engineering issues, including system bottlenecks, integration problems, and data discrepancies. Facilitate root cause analysis and implement long-term solutions.
- – Continuous Improvement: Identify opportunities to enhance data processes, system performance, and data availability. Drive initiatives for automation, process improvement, and cost optimization.
- – Work with experienced team members to conduct root cause analysis of issues, review new and existing code and/or perform unit testing
- – Identify ideas to improve system performance and impact availability
Resolve complex technical design issues - – Analyse user requirements and convert requirements to design documents
- – Make good technical decisions that provide solutions to business challenges
- – Provide comprehensive support for customer success; achieve resolution to outstanding problems or issues
- – Compile timely, comprehensive and accurate documentation and or reports as requested
- – Technology Evaluation and Adoption: Stay up-to-date with the latest industry trends, data engineering tools, and technologies. Evaluate and recommend new tools, frameworks, and solutions to support evolving business needs.
Required Qualifications:
- – B.Sc/HND Computer Science, Computer Engineering or Systems Engineering- Msc is an added advantage
- – Previous data engineering working experience in a fintech (minimum 3 years’ experience)
- – Data Engineering Tools and Technologies, Machine Learning, Relational databases (PostgreSQL, MySQL), NoSQL databases (MongoDB), Cloud storage (AWS S3).
- – Programming Languages: Python (with Django Framework), SQL (PostgresSQL, MySQL), JavaScript (with NodeJS), TypeScript
- – Data Visualisation, Modelling: Tableau, Power BI, Microsoft Excel
- – Excellent communication skills and work ethics