Data engineer at Interswitch Group in Lagos

Position Overview

To operate on big data and data science projects that collect and combine large volumes of data, conduct review, analyze outcomes and patterns and formulate practical findings and strategic guidance to guide strategic judgment and improve Interswitch’s goods and channels. To serve as a strategic thought work collaboratively for cross-operational teams to both produce and assess the viability and effect of new data models and programs.

Duties Data Architecture:

• Advise and teach participants on methods for streamlining and standardising data recording to guarantee quality and precision.
• Build and preserve Interswitch’s data warehouse to assist status updates, evaluation, dimensional modelling, and data development for within and outside the organization participants.
• Empower primary stakeholders to entry dependable, clean business data by emphasising quality and industry standards.
• Recognize and execute enhancements to Interswitch’s data pipeline, which includes better recordkeeping, anomaly discovery, warning, and instrumentation.

Internal Communications:

• Assist teams maximise internal information exchange operational structures by presenting assist and guidance.
• Serve as an support for analytics-based product design, exchanging findings on what is operating and what is not.
• Serve as a train and mentor to team members while nurturing an environment of reciprocal regard and trust.

Education / Expertise

• Tertiary institution First Academic qualification in Computer Science, Information Tech, Statistics, Mathematics, Finance, or associated fields.
• At least 5 years in logical positions, preferably within financial or financial technology institutions.
• A minimum of 3 years of expertise in data science to enable self-sufficient managing of sophisticated conditions and direction to others.

Competencies:

• Data Collection, Evaluation and Regulate: Evaluate practices, obtain, arrange, and process data to fulfil business targets.
• Organizational needs Evaluation: Convert organizational needs into implementable data science initiatives.
• Regulatory framework, Standardized processes and Adherence Management: Formulate, track, and ensure alignment with organisational rules.
• Application Development: Formulate software using programming languages as needed.
• Verbal and Professional writing: Express creative thoughts and strategies transparently, leading others when necessary.
• Architecture: Design architectures meeting system and service qualifications.
• Public speaking: Convey information transparently, succinctly, and compellingly.
• Organizing and Arranging: Prioritise and supervise initiatives productively.
• Evaluate and Status updates: Produce precise and implementable documentation.
• Data Mining and Machine Learning: Extract patterns from large data collections, offer technical direction.
• Client support Dispatch: Meet high client support guidelines and mentor others.
• Project coordination: Lead projects within cost, time, and quality parameters.
• Process Management: Strategize arrange, and implement work productively, using resources like Kanban boards.
• Business Data Modelling: Evaluate, model, and documentation data flow for strategic strategic judgment.
• Metadata Management: Produce and oversee metadata operational structures for information availability.
• DB User Interfaces and Queries: Produce and run queries across various DB interfaces.