Data & AI Agent Analyst Engineer (Cape Town)

We are looking for a Data and AI Agent Analyst to join our team in a hands-on, mid-level role that combines data analytics, business intelligence, AI agent development, and automation. This is an exciting opportunity for someone who enjoys working with data but is equally passionate about exploring what AI can achieve. You will turn complex data into actionable insights, develop reports and dashboards that support business and client decision-making, and build AI agents and automated workflows that make our teams and products faster, smarter, and more efficient.

Responsibilities:

• Develop and maintain business reports, dashboards, and data visualisations.
• Write, optimise, and maintain complex PostgreSQL queries for analysis and reporting.
• Analyse data, identify trends, and deliver actionable insights to stakeholders.
• Ensure data quality, accuracy, validation, and integrity across reporting outputs.
• Design, build, test, and maintain AI agents for analytics, reporting, and operational workflows.
• Develop AI-powered features using LLMs, prompt engineering, RAG, and agent frameworks.
• Integrate AI solutions with APIs, databases, internal systems, and business processes.
• Build automated workflows and proof-of-concepts using Make, Docker, ETL tools, and Python.
• Define evaluation, monitoring, and quality standards for AI agents and automation solutions.
• Partner with business and development teams to deliver practical data, AI, and automation solutions.

Qualifications and experience:

• Bachelor's degree or higher in Data Science, Computer Science, Business Analysis, Finance, Information Systems, or a related field.
• Minimum 5 years' experience in a Data Analyst, BI Analyst, Business Intelligence, or similar data-focused role.
• Hands-on experience building or working closely with AI agents, LLM applications, or automation solutions.
• Strong proficiency in SQL, preferably PostgreSQL.
• Strong proficiency in Python for data cleaning, analysis, automation, and AI solutions.
• Practical understanding of:
• Large language models (LLMs)
• Prompt engineering
• AI agent frameworks
• Retrieval-augmented generation (RAG)
• AI/model evaluation