Find a job 
Featured Companies 
Featured Universities 
For Companies At a Glance
- Tasks: Join a dynamic team to analyse data and build impactful AI solutions.
- Company: Amach, a fast-growing tech company with a focus on innovation.
- Benefits: Flexible working, competitive salary, and opportunities for career advancement.
- Why this job: Make a real difference in data-driven decision-making and work with cutting-edge technology.
- Qualifications: Experience in data analysis, SQL, Python, and data visualisation tools.
- Other info: Inclusive culture with a commitment to diversity and personal growth.
The predicted salary is between 30000 - 50000 £ per year.
Amach is an industry-leading technology driven company with headquarters located in Dublin and remote teams in the UK and Europe. Our blended teams of local and nearshore talent are optimised to deliver high quality and collaborative solutions. Established in 2013, we specialise in cloud migration and development, digital transformation including agile software development, DevOps, automation, data and machine learning.
We are hiring a senior, hands-on Data Analyst / Data Engineer to support the delivery of AI-enabled, decision-support solutions within a large, complex operational environment, with designs that scale across multiple operating companies. This role sits across advanced analytics and data engineering, with work flexing depending on the delivery phase and workstream. You will operate as a senior individual contributor, embedded within a cross-functional product squad alongside Data Scientists, Visualisation Engineers, and change teams. The role is highly hands-on and delivery-focused, suited to someone who enjoys deep problem-solving, data exploration and building production-ready analytical assets.
Please note this role operates in a hybrid model with candidates expected to be able and willing to work from our customer’s London office three times per week.
Responsibilities:- Strong experience in data analysis, analytics engineering or data engineering within a product or delivery-focused environment.
- Advanced skills in SQL and data processing using Python (e.g. Pandas).
- Hands-on experience developing and optimising data pipelines for analytics and reporting use cases.
- Experience working with data visualisation tools such as PowerBI, Tableau, or similar.
- Proven ability to understand, assess and modernise legacy datasets and pipelines.
- Strong understanding of data modelling and API integration.
- Experience developing, testing and deploying production data solutions (not just PoCs).
- Familiarity with cloud platforms (AWS preferred) and working knowledge of DevOps concepts (CI/CD, version control).
- Comfortable working independently and communicating with non-technical stakeholders.
- Strong stakeholder engagement and solution-oriented mindset.
- Ability to deliver high-impact outcomes under tight timelines.
- Experience working in advisory or consultancy-style delivery settings.
- Discover, connect to, and analyse data from a wide range of sources, including relational databases and flat files (CSV, YML, XLS etc.).
- Identify, investigate and remediate data quality, completeness, and consistency issues.
- Challenge data provenance, assumptions and definitions within legacy datasets to ensure they are fit for modern analytics and AI use cases.
- Translate business questions into clear analytical approaches, KPIs, metrics and data narratives.
- Support the definition of KPIs and analytical logic that underpin dashboards and operational reporting.
- Design, develop, and optimise data pipelines for ingestion, transformation, and storage.
- Ensure data pipelines are production-ready, reliable, scalable, and maintainable beyond proof-of-concept.
- Implement best practices for data quality, integrity, security, performance and scalability in cloud environments.
- Support multi-OpCo deployment by designing modular, interoperable data architectures and pipelines.
- Collaborate with Data Scientists to prepare, validate, and structure datasets that support advanced analytics and AI-driven solutions.
- Support the integration of analytics and AI outputs into live operational workflows, ensuring outputs are actionable and adopted.
- Willingness to travel internationally during later stages to support group-wide deployment.
- Familiarity with airline or logistics data domains.
- Ability to implement standards and frameworks for scalable data solutions across multiple operating companies.
- An opportunity to join a fast-growing company.
- Options for career advancement.
- Learning and development opportunities.
- Flexible working environment.
- Competitive salaries based on experience.
Amach is an equal opportunity employer and makes employment decisions on the basis of merit. We celebrate diversity and are committed to creating an inclusive environment for all employees.
This job description is intended to convey essential responsibilities and qualifications for this role, but it is not an exhaustive list of tasks that an employee may be required to perform.
At Amach, we strive to be an inclusive community of open-minded individuals with different backgrounds and we are committed to fostering, cultivating and preserving a culture of diversity, equity and inclusion.
Data Analyst / Data Engineer in London employer: Amach
Contact Detail:
Amach Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Analyst / Data Engineer in London
✨Tip Number 1
Network like a pro! Reach out to your connections on LinkedIn or attend industry meetups. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your data projects, especially those involving SQL and Python. This gives potential employers a taste of what you can do and sets you apart from the crowd.
✨Tip Number 3
Prepare for interviews by practising common data-related questions. Think about how you would explain complex concepts to non-technical stakeholders. This will help you shine during those crucial conversations.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, we love seeing candidates who are proactive about their job search!
We think you need these skills to ace Data Analyst / Data Engineer in London
Some tips for your application 🫡
Tailor Your Application: Make sure to customise your CV and cover letter for the Data Analyst / Data Engineer role. Highlight your experience with SQL, Python, and data pipelines, as these are key skills we're looking for!
Show Off Your Problem-Solving Skills: In your application, share examples of how you've tackled complex data challenges in the past. We love candidates who can demonstrate their analytical thinking and hands-on approach.
Be Clear and Concise: When writing your application, keep it straightforward. Use clear language to explain your experience and how it relates to the responsibilities listed in the job description. We appreciate clarity!
Apply Through Our Website: Don't forget to submit your application through our careers page! It’s the best way for us to receive your details and get you into our hiring process smoothly.
How to prepare for a job interview at Amach
✨Know Your Data Tools
Make sure you brush up on your SQL and Python skills, especially with libraries like Pandas. Be ready to discuss how you've used these tools in past projects, particularly in developing and optimising data pipelines.
✨Understand the Business Context
Before the interview, take some time to research Amach and its focus on cloud migration and digital transformation. Think about how your analytical skills can translate business questions into actionable insights that align with their goals.
✨Showcase Your Problem-Solving Skills
Prepare examples of complex problems you've solved using data analysis. Highlight your approach to identifying data quality issues and how you’ve modernised legacy datasets to fit current analytics needs.
✨Engage with Stakeholders
Since this role involves communicating with non-technical stakeholders, practice explaining technical concepts in simple terms. Be ready to discuss how you've collaborated with cross-functional teams to deliver impactful data solutions.
