Junior Cloud Data Engineer - Ipswich, Norwich, Cambridge, Chelmsford

Birketts
Cambridge
1 month ago
Applications closed

Related Jobs

View all jobs

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer - Azure

Data Engineer (18 Months FTC)

Senior Data Engineer

Data Engineer

Junior Cloud Data Engineer - Ipswich, Norwich, Cambridge, Chelmsford Term Type: PermanentDepartment: Business Systems 

Birketts is a full service, UK Top 50 law firm. With a heritage spanning 160 years, we have more than 700 lawyers and legal professionals based in Bristol, Cambridge, Chelmsford, Ipswich, London, Norwich and Sevenoaks. We advise businesses, government and public sector organisations and individuals in the UK and internationally across four principal practice groups: Real Estate, Corporate Services, Dispute Resolution and Private Client.

We are defined by our Next Level Law proposition. We work with our clients as a proactive partner, horizon scanning and thinking ahead to the changes, challenges or opportunities that they may face.

Next Level Law is also applied to our people. Our collegiate culture means everyone is encouraged to achieve their next level in everything they do.  RollOnFriday recently ranked us as the 5th best law firm to work at in 2024.

With our ambition to succeed, comes a strong desire to make a positive contribution to the communities we serve, and we are committed to delivering the objectives set out in our ESG strategy. Diversity plays an integral part in all that we do, with female partners comprising 40% of our partnership.

As Junior Cloud Data Engineer, you will support the design, development, and maintenance of cloud-based data solutions. You will work closely with senior engineers to build and optimise data pipelines, assist in data integration projects, and contribute to the overall efficiency of our cloud data / Hybrid infrastructure.

This role offers a fantastic opportunity to gain hands-on experience with cloud platforms, databases, and data processing technologies while developing expertise in modern data engineering practices.

  • A degree in Computer Science, Data Science, Information Technology, or a related field, OR equivalent experience.
  • Basic understanding of cloud platforms (Azure, AWS, or GCP) and a willingness to develop expertise in cloud data engineering.
  • Some experience or academic exposure to SQL, including writing queries and optimising data retrieval.
  • Familiarity with ETL/ELT processes and data integration concepts.
  • Interest in working with big data technologies and cloud-based data warehouses (e.g., Snowflake, Fabric).
  • Strong problem-solving skills and the ability to learn and adapt quickly in a fast-paced environment.
  • Basic programming knowledge (e.g., Python, SQL, or PowerShell) is a plus.
  • Excellent communication skills, with the ability to collaborate effectively with technical and non-technical teams.
  • A proactive, curious mindset, eager to take on new challenges and grow professionally.
  • Ability to work under pressure and meet strict deadlines.
  • Be able to travel as necessary, and perform additional job-related duties as requested.
  • Communicate effectively in both verbal (i.e. day-to-day discussions, team meetings) and written (requirements and design specifications) form, as well as have an overall ability to be clear and concise in all communications.
  • Positive and enthusiastic approach to team work.
  • Self-motivated, ability to use initiative and provide pro-active support to users.
  • Commitment to ongoing learning and development.
  • Assist in designing and developing cloud-based data pipelines to collect, process, and store data efficiently.
  • Support ETL/ELT processes, ensuring data is properly structured and integrated across various systems.
  • Work with SQL databases to query, clean, and transform data for analytics and reporting.
  • Collaborate with senior engineers to implement cloud data solutions using platforms like Azure, AWS, or Google Cloud.
  • Monitor and maintain data infrastructure, helping to identify and resolve performance issues.
  • Provide 3rd-line support for cloud data-related incidents, troubleshooting and resolving technical issues under guidance.
  • Assist in implementing data security best practices and ensuring compliance with relevant regulations.
  • Document processes, workflows, and best practices to support ongoing knowledge sharing and training.
  • Stay up to date with emerging cloud and data engineering trends and apply learnings to improve existing solutions.

At Birketts, our culture is driven by ambition and a commitment to positively impact all the communities we serve. We are dedicated to the success, development, and wellbeing of our colleagues, helping them achieve their goals and seize the opportunities that come with our growth. Alongside a flexible and inclusive work environment, we offer the following core benefits:

  • 25 days holiday (FTE) plus Bank Holidays 
  • Long Service holiday award – 1 extra week every 10 years continuous service
  • Private Healthcare with BUPA (offered after probation is passed)
  • Scottish Widows Pension Scheme (5% employer / 5% Employee)
  • Staff Profit Share and Individual Performance Bonus Scheme 
  • Salary sacrifice (Pensions, Staff Profit Share)
  • Life Assurance - 4 x salary / Permanent Health Insurance
  • Paid CSR Day
  • Enhanced Maternity/Paternity Leave
  • Subsidised gym membership
  • Electric car scheme
  • Agile/Hybrid Working Policy
  • Dress for your Day Policy

Birketts is a flexible business which has embraced a hybrid working model where our colleagues enjoy a mix of home and office working. We welcome applications from people looking for flexible, agile, and part-time roles and we are happy to explore your preferred working patterns as part of your application. 

Please note that this job profile is not an exhaustive list of duties but merely an outline of the key components of the role. You may be required by your line manager to take on additional responsibilities when requested.

Birketts is committed to being an Equal Opportunity Employer. Our policy is unequivocal: we do not tolerate discrimination based on age, disability, sex, race, religion or belief, gender reassignment, marriage or civil partnership, pregnancy or maternity, or sexual orientation.

We pride ourselves on being an inclusive organisation that actively promotes equality of opportunity for all, valuing the right mix of talent, skills, and potential. We welcome applications from a diverse range of candidates, and selection for roles is based solely on individual merit.

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.

How Many Data Science Tools Do You Need to Know to Get a Data Science Job?

If you’re trying to break into data science — or progress your career — it can feel like you are drowning in names: Python, R, TensorFlow, PyTorch, SQL, Spark, AWS, Scikit-learn, Jupyter, Tableau, Power BI…the list just keeps going. With every job advert listing a different combination of tools, many applicants fall into a trap: they try to learn everything. The result? Long tool lists that sound impressive — but little depth to back them up. Here’s the straight-talk version most hiring managers won’t explicitly tell you: 👉 You don’t need to know every data science tool to get hired. 👉 You need to know the right ones — deeply — and know how to use them to solve real problems. Tools matter, but only in service of outcomes. So how many data science tools do you actually need to know to get a job? For most job seekers, the answer is not “27” — it’s more like 8–12, thoughtfully chosen and well understood. This guide explains what employers really value, which tools are core, which are role-specific, and how to focus your toolbox so your CV and interviews shine.

What Hiring Managers Look for First in Data Science Job Applications (UK Guide)

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

The Skills Gap in Data Science Jobs: What Universities Aren’t Teaching

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.