Data & AI Engineer Intern (JHB Hybrid)

ENVIRONMENT:

SEEKING a career in data? Then a provider of tailored Financial Solutions has a sterling growth opportunity for an ambitious Data & AI Engineer Intern to join a structured, mentored pathway across its Data & AI team: Data Engineering first, with applied AI alongside it and exposure to the analytics stream. You will do real, supervised work on a governed production estate, covering data preparation, pipeline support, reporting, quality checks, AI-workload data preparation, and documentation, all under a defined training plan with measurable milestones. Applicants will require a basic understanding of SQL and relational data concepts; foundational exposure to Python or another programming language (coursework or projects count) and must be comfortable with spreadsheets, data structures, and basic analytical thinking. You also need to be aware that data is sensitive and that confidentiality is non-negotiable.

DUTIES:

· Assist with data extraction, cleaning, validation, and preparation for analytics and reporting use cases.

· Write and support SQL queries, Python scripts, and repeatable data processing tasks under supervision.

· Help build and maintain dashboards and reports in Power BI and SSRS.

· Support monitoring of scheduled data jobs: spot failures, gather the facts, and log them properly.

· Participate in data quality checks, reconciliations, and issue logging, learning why trust in data matters.

· Contribute to documentation: source-to-target mappings, process notes, data dictionaries, and testing evidence.

· Learn the company’s governance, security, privacy, and change management standards from day one.

· Attend team ceremonies and give clear, honest progress updates on assigned tasks.

What you’ll work with –

Ø SQL Server and Microsoft Fabric, the governed data estate you will learn on.

Ø Power BI and SSRS, which make up the reporting layer.

Ø Python and Git, giving you modern engineering habits from the start.

Ø Medallion architecture, which is how raw data becomes trusted, reportable data.

Ø A governed in-house AI platform, where you will learn to use AI tools properly, with controls, from week one.

Ø Evaluation and human-review workflows, which are how we decide whether an AI output is good enough to use and how that decision is evidenced.

REQUIREMENTS:

· Basic understanding of SQL and relational data concepts.

· Foundational exposure to Python or another programming language (coursework or projects count).

· Comfort with spreadsheets, data structures, and basic analytical thinking.

· Attention to detail and willingness to document work clearly.

· Good written and verbal communication, and genuine appetite to learn.

· Awareness that data is sensitive and that confidentiality is non-negotiable.

Advantageous –

· Qualification in progress or recently completed in Data Science, Computer Science, Information Systems, Statistics, Mathematics, Engineering, or related.

· Any exposure to BI tools, cloud platforms, Git, notebooks, or ETL concepts.

· Portfolio, coursework, hackathon, or capstone evidence of data problem-solving. Show anything you have built.

· Curiosity about how a regulated financial-services business actually runs on data.

ATTRIBUTES:

· You learn fast and enjoy it, because new tools and feedback energise you rather than intimidate you.

· You verify before you trust, and that includes AI output above all.

· You are curious about how businesses actually run on data.

· You check your work and ask when unsure, with no silent guessing.

· You take small tasks seriously and build trust through follow-through.

· You want a career in data, not just a gap-filler job.

· Integrity and values, with the ability to handle sensitive and confidential information.

· Attention to detail and a results-driven quality mindset.

· Strong problem-solving; composure in a fast-moving, dynamic environment.

· Works well in a team and independently; communicates openly.

· Growth mindset, actively wanting to learn and develop.