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For Companies At a Glance
- Tasks: Lead data transformation projects and solve complex data challenges across teams.
- Company: Major UK organisation focused on innovative data solutions.
- Benefits: Competitive daily rate, hybrid working, and a dynamic work environment.
- Why this job: Shape the future of data architecture and make impactful decisions.
- Qualifications: Strong data engineering skills and experience in fast-paced environments.
- Other info: Opportunity for career growth in a collaborative, cross-functional setting.
A major UK organisation is undertaking a large-scale data warehouse re-architecture and migration programme and is looking for a Lead Analytics Engineer to join on a long-term contract. This is a hands-on, highly cross-functional role where you will work across multiple business and technical teams to solve complex data problems. You will play a key role in shaping the next generation of data assets, helping move from legacy architecture to a simpler, more scalable, and analytics-friendly platform.
This programme is not a lift-and-shift migration. It focuses on rethinking how data is modelled, accessed, and used, with an emphasis on clarity, scalability, and enabling faster, better decision-making. We are particularly interested in individuals who have thrived in less structured, fast-moving environments — where ownership, problem-solving, and adaptability are critical.
What You’ll Do
- Work across multiple teams and business domains to understand problems and deliver effective data solutions
- Design and build scalable, well-structured data models in a modern warehouse environment
- Translate complex requirements into clear, maintainable SQL-based transformations
- Lead data migrations, including historical backfills and zero-downtime cutovers
- Collaborate closely with Analytics Engineers, Data Scientists, Product, and Engineering teams
- Help define and evolve data standards, modelling approaches, and best practices
- Enable teams by delivering high-quality, trusted datasets that are easy to use and understand
- Clearly articulate trade-offs in tooling and design decisions, explaining not just what to use, but why
Technical Environment
- dbt (SQL-first modelling)
- Airflow orchestration
- BigQuery (or similar cloud data warehouse such as Snowflake)
- Data sourced from transactional, operational, and product systems
- Mix of open-source, cloud, and internal tooling
Required Experience
- Strong experience in data modelling, ETL/ELT, and building scalable data pipelines
- Advanced SQL skills and deep understanding of data warehousing principles
- Experience working with large-scale datasets
- Hands-on experience with dbt and modern data stack tooling
- Experience designing data solutions that support analytics and reporting use cases
- Background can be from any industry (not limited to financial services), provided strong data engineering/analytics experience
What We’re Looking For
- Experience working in dynamic, less structured environments (e.g. startups, scale-ups, or transformation programmes)
- Strong cross-functional mindset — comfortable working across teams to solve problems
- Ability to rationalise technical decisions, clearly explaining the reasoning behind tools and approaches
- Excellent stakeholder management skills, with the ability to influence both technical and non-technical audiences
- Clear, concise communicator—able to explain complex ideas simply and effectively
- Proactive, adaptable, and comfortable taking ownership in ambiguous situations
Data Engineer in City of London employer: iDPP
Contact Detail:
iDPP Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Data Engineer in City of London
✨Tip Number 1
Network like a pro! Get out there and connect with folks in the industry. Attend meetups, webinars, or even just grab a coffee with someone who’s already in the data engineering game. You never know where a casual chat might lead!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving data modelling and ETL processes. This is your chance to demonstrate your hands-on experience and problem-solving abilities in a way that a CV just can’t capture.
✨Tip Number 3
Prepare for interviews by brushing up on your SQL and data warehousing principles. Be ready to discuss your past experiences in detail, especially how you’ve tackled complex data problems. Remember, they want to see how you think and approach challenges!
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for talented individuals who thrive in dynamic environments. Your next big opportunity could be just a click away, so make sure you get your application in!
We think you need these skills to ace Data Engineer in City of London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV speaks directly to the role of Lead Data Analytics Engineer. Highlight your experience with data modelling, ETL/ELT, and any hands-on work with dbt or similar tools. We want to see how your skills align with our needs!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're the perfect fit for this dynamic role. Share specific examples of how you've thrived in fast-paced environments and tackled complex data problems.
Showcase Your Problem-Solving Skills: In your application, don’t just list your skills—show us how you’ve used them! Describe situations where you’ve solved tricky data challenges or improved processes. We love seeing real-world applications of your expertise.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows you’re keen on joining our team at StudySmarter!
How to prepare for a job interview at iDPP
✨Know Your Data Inside Out
Before the interview, make sure you’re well-versed in data modelling, ETL/ELT processes, and the specific tools mentioned in the job description, like dbt and BigQuery. Brush up on your SQL skills and be ready to discuss how you've used these technologies in past projects.
✨Showcase Your Problem-Solving Skills
Prepare examples of how you've tackled complex data problems in less structured environments. Think about specific challenges you faced, the solutions you implemented, and the impact they had. This will demonstrate your adaptability and ownership in dynamic settings.
✨Communicate Clearly and Concisely
Practice explaining complex technical concepts in simple terms. You might be asked to articulate trade-offs in tooling and design decisions, so being able to communicate your thought process clearly will impress both technical and non-technical interviewers.
✨Collaborate and Connect
Since this role involves working across multiple teams, be prepared to discuss your experience in cross-functional collaboration. Highlight instances where you’ve successfully worked with Analytics Engineers, Data Scientists, or other stakeholders to deliver effective data solutions.