Data Engineer

JR United Kingdom; Northampton; On-site; GBP 45,000 - 65,000; Full time

Job summary

A leading data consultancy firm is seeking a Data Engineer in Northampton to join their Professional Services division. You will collaborate with diverse stakeholders to leverage data techniques and AI for business value. The ideal candidate will have experience in Azure and familiarity with distributed systems, bringing innovative solutions to enhance efficiency and scalability.

Benefits

25 days holiday

Flexible working options

Mental health and wellbeing support

Annual wellbeing allowance

Employee benefits

Generous pension contribution

Ongoing professional development

Qualifications

• Experience with Azure services such as ADF, Azure Databricks, and SQL DWH is essential.
• Familiarity with distributed systems and semi-structured/unstructured data formats.
• Knowledge of machine learning frameworks and operationalising models.

Responsibilities

• Collaborate with experts to derive insights from various data sources.
• Utilise AI techniques for enhanced decision-making and customer outcomes.
• Design and implement new data collection processes for accuracy and relevance.

Skills

Collaboration

Communication

Problem Solving

Analytical Thinking

Detail-Oriented

Lead by Example

Adaptability

Tools

Azure

Spark

Databricks

MLOps

Job description

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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:

• Collaborating with business subject matter experts to discover valuable insights in structured, semi-structured, and unstructured data sources.
• Using data engineering and AI techniques to help clients make smarter decisions, reduce service failures, and deliver better customer outcomes.
• Connecting to and extracting data from source systems, applying business logic and transformations, and enabling data-driven decision-making.
• Supporting strategic planning and identifying opportunities to apply AI models or machine learning techniques to enhance business processes.
• Capturing data requirements from customer and technical teams.
• Designing and implementing new data collection processes that ensure completeness, quality, and business relevance.
• Developing innovative ways of working to improve efficiency and scalability.
• Setting up interfaces to source systems and/or collaborating with system owners.
• Building, orchestrating, and optimisingdata and AI pipelines.
• Diagnosing root causes of poor data quality and working with data owners to resolve them.
• Securing and managing data access.
• Supporting data science teams and other users in data acquisition and preparation.
• Creating robust data models and deploying them into production.
• Ensuring 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.
• 25 Days holiday (excluding bank holidays) – increasing by a day every 2 years.
• Flexible working options – hybrid.
• Mental health and wellbeing support, including access to counselling.
• Annual wellbeing allowance (e.g. personal training, fitness, wellness apps).
• Up to 10% of your salary in employee benefits, including critical illness cover, life insurance, and private healthcare (post-probation).
• Generous company pension contribution.
• Ongoing professional development and training opportunities.