Data Engineer

Due to continued growth, we are currently looking for a Data Engineer to join our Professional Services division. You will be part of a cross-functional Data Consulting team spanning data engineering, data science, AI, analytics, and visualisation.

You will work with clients across multiple sectors, helping them explore next-generation data techniques, AI capabilities, and tools to drive measurable business value from their data assets.

A day in the life of an Aiimi Data Engineer:

• Collaborate with business subject matter experts to discover valuable insights in structured, semi-structured, and unstructured data sources.
• Use data engineering and AI techniques to help clients make smarter decisions, reduce service failures, and deliver better customer outcomes.
• Connect to and extract data from source systems, apply business logic and transformations, and enable data-driven decision-making.
• Support strategic planning and identify opportunities to apply AI models or machine learning techniques to enhance business processes.
• Capture data requirements from customer and technical teams.
• Design and implement new data collection processes that ensure completeness, quality, and business relevance.
• Develop innovative ways of working to improve efficiency and scalability.
• Set up interfaces to source systems and collaborate with system owners.
• Build, orchestrate, and optimise data and AI pipelines.
• Diagnose root causes of poor data quality and work with data owners to resolve them.
• Secure and manage data access.
• Support data science teams and other users in data acquisition and preparation.
• Create robust data models and deploy them into production.
• Ensure models, reports, and architectures are promoted to centralised, self-service platforms.

Requirements

• Collaboration: excited to work alongside subject matter experts, data scientists, AI specialists, analysts, and visualisation professionals.
• Communication: able to explain complex technical concepts (including AI and machine learning outcomes) to non-technical audiences.
• Problem Solving: using data and AI as a foundation to tackle business challenges.
• Analytical Thinking: breaking down complex problems into manageable, actionable components.
• Detail-Oriented: maintaining high-quality outputs under tight deadlines.
• Lead by Example: inspiring clients to embrace new technologies, AI innovations, and modern data practices.
• Adaptability: understanding legacy processes while introducing and championing new technology.

Technologies / Tools

• Experience with Azure (ADF, Azure Databricks, Data Lake Storage, SQL DWH) or other cloud platforms (essential).
• Familiarity with distributed systems (Spark, Databricks, etc.).
• Familiarity with semi-structured and unstructured data formats.
• Knowledge of machine learning frameworks and how to operationalise models in production.
• Understanding of MLOps and AI model lifecycle management is a plus.

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