National AI Awards 2025Discover AI's trailblazers! Join us to celebrate innovation and nominate industry leaders.

Nominate & Attend

Data Analyst/Engineer, London

Axiom Software Solutions
London
3 months ago
Applications closed

Related Jobs

View all jobs

Grafana specialist - Senior Data Engineer/ Business Analyst. Trading Firm. 6 month rolling con[...]

Lead Data Engineer

Level 7 Data & Ai Coach

Senior Data Engineer (Maximo)

Lead Data engineer, London

Data Scientist

Data Analyst/Engineer
Role: Data Analyst/Engineer

Type : Contractors

Duration : 3 to 6 months to start with

Location : UK, Remote

Senior Level Data Engineer/Data Analyst technical lead with data analytics experience, Databricks, Pyspark and Python

This is a key role that requires senior/lead with great communication skills who is very proactive with risk issue management.

Experience and Education Required

10+ years of experience as Data Analyst/Data Engineer/Data Scientist with Databricks on AWS expertise in designing and implementing scalable, secure, and cost-efficient data solutions on AWS

Job Profile:

Hands-on data analytics experience with Databricks on AWS, Pyspark and Python

Must have prior experience with migrating a data asset to the cloud using a GenAI automation option

Experience in migrating data from on-premises to AWS

Expertise in developing data models, delivering data-driven insights for business solutions

Experience in pretraining, fine-tuning, augmenting and optimizing large language models (LLMs)

Experience in Designing and implementing database solutions, developing PySpark applications to extract, transform, and aggregate data, generating insights

Data Collection Integration: Identify, gather, and consolidate data from diverse sources, including internal databases and spreadsheets ensuring data integrity and relevance.

Data Cleaning Transformation: Apply thorough data quality checks, cleaning processes, and transformations using Python (Pandas) and SQL to prepare datasets.

Automation Scalability: Develop and maintain scripts that automate repetitive data preparation tasks.

Autonomy Proactivity: Operate with minimal supervision, demonstrating initiative in problem-solving, prioritizing tasks, and continuously improving the quality and impact of your work

Technical Skills:

Minimum of 10 years of experience as a Data Analyst, Data Engineer, or related role, ideally with a bachelors degree or higher in a relevant field.

Strong proficiency in Python (Pandas, Scikit-learn, Matplotlib) and SQL, with experience working across various data formats and sources.

Proven ability to automate data workflows, implement code-based best practices, and maintain documentation to ensure reproducibility and scalability.

Behavioral Skills:

Ability to manage in tight circumstances, very pro-active with risk issue management

Requirement Clarification Communication: Interact directly with colleagues to clarify objectives, challenge assumptions.

Documentation Best Practices: Maintain clear, concise documentation of data workflows, coding standards, and analytical methodologies to support knowledge transfer and scalability.

Collaboration Stakeholder Engagement: Work closely with colleagues who provide data, raising questions about data validity, sharing insights, and co-creating solutions that address evolving needs.

Excellent communication skills for engaging with colleagues, clarifying requirements, and conveying analytical results in a meaningful, non-technical manner.

Python, Pyspark, Databricks

National AI Awards 2025

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Return-to-Work Pathways: Relaunch Your Machine Learning Career with Returnships, Flexible & Hybrid Roles

Returning to work after an extended break can feel like starting from scratch—especially in a specialist field like machine learning. Whether you paused your career for parenting, caring responsibilities or another life chapter, the UK’s machine learning sector now offers a variety of return-to-work pathways. From structured returnships to flexible and hybrid roles, these programmes recognise the transferable skills and resilience you’ve developed, pairing you with mentorship, upskilling and supportive networks to ease your transition back. In this guide, you’ll discover how to: Understand the current demand for machine learning talent in the UK Leverage your organisational, communication and analytical skills in ML contexts Overcome common re-entry challenges with practical solutions Refresh your technical knowledge through targeted learning Access returnship and re-entry programmes tailored to machine learning Find roles that fit around family commitments—whether flexible, hybrid or full-time Balance your career relaunch with caring responsibilities Master applications, interviews and networking specific to ML Learn from inspiring returner success stories Get answers to common questions in our FAQ section Whether you aim to return as an ML engineer, research scientist, MLOps specialist or data scientist with an ML focus, this article will map out the steps and resources you need to reignite your machine learning career.

LinkedIn Profile Checklist for Machine Learning Jobs: 10 Tweaks to Drive Recruiter Interest

The machine learning landscape is rapidly evolving, with demand soaring for experts in modelling, algorithm tuning and data-driven insights. Recruiters hunt for candidates proficient in Python, TensorFlow, PyTorch and MLOps processes. A generic profile simply won’t cut it. Our step-by-step LinkedIn for machine learning jobs checklist covers 10 targeted tweaks to ensure your profile ranks in searches and communicates your technical impact. Whether launching your ML career or seeking leadership roles, these optimisations will sharpen your professional narrative and boost recruiter engagement.

Part-Time Study Routes That Lead to Machine Learning Jobs: Evening Courses, Bootcamps & Online Masters

Machine learning—a subset of artificial intelligence—enables computers to learn from data and improve over time without explicit programming. From predictive maintenance in manufacturing to recommendation engines in e-commerce and diagnostic tools in healthcare, machine learning (ML) underpins many of today’s most innovative applications. In the UK, demand for ML professionals—engineers, data scientists, research scientists and ML operations specialists—is growing rapidly, with roles projected to increase by over 50% in the next five years. However, many aspiring ML practitioners cannot step away from work or personal commitments for full-time study. Thankfully, a rich ecosystem of part-time learning pathways—Evening Courses, Intensive Bootcamps and Flexible Online Master’s Programmes—empowers you to learn machine learning while working. This comprehensive guide examines each route: foundational CPD units, immersive bootcamps, accredited online MSc programmes, funding options, planning strategies and a real-world case study. Whether you’re a software developer branching into ML, a statistician aiming to upskill, or a professional exploring AI-driven innovation, you’ll discover how to build in-demand ML expertise on your own schedule.