At a Glance
- Tasks: Lead the design and build of modern data platforms and high-performance pipelines.
- Company: Join Simple Machines, a global tech consultancy shaping the future of data engineering.
- Benefits: Enjoy autonomy, competitive salary, and opportunities for professional growth.
- Other info: Collaborate with senior engineers in a dynamic, innovative environment.
- Why this job: Tackle high-impact data challenges and work with cutting-edge technologies.
- Qualifications: Strong Python, SQL, and experience with large-scale data platforms required.
The predicted salary is between 80000 - 100000 £ per year.
Simple Machines is a global, independent technology consultancy operating across Sydney, New Zealand, London, Poland and San Francisco. We design and build modern data platforms, intelligent systems, and bespoke software at the intersection of Data Engineering, Software Engineering and AI.
We work with enterprises, scale-ups, and government to turn messy, high-value data into products, platforms, and decisions that actually move the needle. We don’t do generic. We build things that matter - We engineer data to life™.
The Role
This is a hands-on principal engineering role, not an architecture-only seat and not a support function. You’ll be responsible for technical direction, platform design and architectural decision-making. You will design and build greenfield data platforms, real-time pipelines, and data products for clients who are serious about using data properly. You’ll work in small, high-calibre teams and operate close to both the problem and the client.
If you enjoy solving hard data problems, shaping modern architectures (data mesh, data products, contracts), and delivering real outcomes — this is your lane.
What You’ll Be Doing
- Lead Platform & Architecture Design
- Own the end-to-end architecture of modern, cloud-native data platforms
- Design scalable data ecosystems using data mesh, data products, and data contracts
- Make high-impact architectural decisions across ingestion, storage, processing, and access layers
- Ensure platforms are secure, compliant, and production-grade by design
- Build Modern Data Platforms
- Design and deliver cloud-native data platforms using Databricks, Snowflake, AWS, and GCP
- Apply modern architectural patterns: data mesh, data products, and data contracts
- Integrate deeply with client systems to enable scalable, consumer-oriented data access
- Develop High-Performance Pipelines
- Build and optimise batch and real-time pipelines
- Work with streaming and event-driven tech such as Kafka, Flink, Kinesis, Pub/Sub
- Orchestrate workflows using Airflow, Dataflow, Glue
- Work at Scale
- Process and transform large datasets using Spark and Flink
- Design systems that perform in production - not just on paper
- Own Data Storage & Performance
- Work across relational, NoSQL, and analytical stores (Postgres, BigQuery, Snowflake, Cassandra, MongoDB)
- Optimise storage formats and access patterns (Parquet, Delta, ORC, Avro)
- Cloud, Security & Governance
- Implement secure, compliant data solutions with security by design
- Embed governance without killing developer velocity
- Consult and Influence
- Work directly with clients to understand problems and shape solutions
- Translate business needs into pragmatic engineering decisions
- Act as a trusted technical advisor, not just an order taker
- Technical Leadership & Quality
- Set engineering standards, patterns, and best practices across teams
- Review designs and code, providing clear technical direction and mentorship
- Raise the bar on data quality, testing, observability, and operational excellence
What We’re Looking For
- Core Engineering Strength
- Strong Python and SQL
- Deep experience with Spark and modern data platforms (Databricks / Snowflake)
- Solid grasp of cloud data services (AWS or GCP)
- Architecture & Design Judgement
- Demonstrated ownership of large-scale data platform architectures
- Strong data modelling skills and architectural decision-making ability
- Comfortable balancing trade-offs between performance, cost, and complexity
- Data Platform Experience
- Built and operated large-scale data pipelines in production
- Strong data modelling capability and architectural judgement
- Comfortable with multiple storage technologies and formats
- Engineering Discipline
- Infrastructure-as-code experience (Terraform, Pulumi)
- CI/CD pipelines using tools like GitHub Actions, ArgoCD
- Data testing and quality frameworks (dbt, Great Expectations, Soda)
- Delivery & Consulting Mindset
- Experience in consulting or professional services environments
- Strong consulting instincts — able to challenge assumptions and guide clients toward better outcomes
- Comfortable mentoring senior engineers and influencing technical culture
Benefits
Why Simple Machines: You’ll work on interesting, high-impact problems. You’ll build modern platforms, not maintain legacy mess. You’ll be surrounded by senior engineers who actually know their craft. You’ll have autonomy, influence, and room to grow. If you’re a senior data engineer who wants to build properly, think clearly, and deliver real outcomes - we should talk.
Principal Data Engineer in London employer: Simple Machines
At Simple Machines, we pride ourselves on being an exceptional employer, offering a dynamic work culture that fosters innovation and collaboration in the heart of London. Our commitment to employee growth is reflected in our comprehensive training programmes and mentorship opportunities, ensuring that our team members thrive in their careers while working on impactful data and AI projects. Join us to be part of a forward-thinking consulting firm that values quality, client success, and the professional development of its employees.
StudySmarter Expert Advice🤫
We think this is how you could land Principal Data Engineer in London
✨Get Involved in Data Science Meetups
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Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Simple Machines.
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We think you need these skills to ace Principal Data Engineer in London
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Simple Machines, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Simple Machines. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Simple Machines
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Simple Machines!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
