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 that fosters a culture of innovation and collaboration. Our team members enjoy the autonomy to tackle high-impact data challenges while working alongside experienced engineers in a supportive environment. With opportunities for professional growth and the chance to build cutting-edge data platforms in vibrant locations like Sydney and London, we empower our employees to make meaningful contributions that truly matter.
StudySmarter Expert Advice🤫
We think this is how you could land Principal Data Engineer in London
✨Tip Number 1
Network like a pro! Reach out to your connections in the data engineering field and let them know you're on the lookout for opportunities. Attend meetups or webinars related to data platforms and AI to meet potential employers and learn about job openings.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving modern data platforms and real-time pipelines. This will give you an edge when chatting with potential employers and demonstrate your hands-on experience.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge. Be ready to discuss your experience with tools like Databricks, Snowflake, and cloud services. Practice explaining complex concepts in simple terms, as you'll need to communicate effectively with clients.
✨Tip Number 4
Don't forget to apply through our website! We love seeing candidates who are genuinely interested in joining us at Simple Machines. Tailor your application to highlight how your skills align with our mission of building modern data platforms that matter.
We think you need these skills to ace Principal Data Engineer in London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that align with the Principal Data Engineer role. Highlight your hands-on experience with data platforms, cloud services, and any relevant projects that showcase your ability to solve complex data problems.
Craft a Compelling Cover Letter:Use your cover letter to tell us why you're passionate about data engineering and how your background makes you a great fit for our team. Share specific examples of your work that demonstrate your technical leadership and problem-solving skills.
Showcase Your Technical Skills:Don’t shy away from listing your technical proficiencies, especially in Python, SQL, and tools like Spark or Databricks. We want to see your expertise shine through, so include any relevant certifications or projects that highlight your capabilities.
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 us you’re keen on joining our team!
How to prepare for a job interview at Simple Machines
✨Know Your Tech Stack
Make sure you’re well-versed in the technologies mentioned in the job description, like Python, SQL, Spark, and cloud services like AWS or GCP. Brush up on your knowledge of data platforms like Databricks and Snowflake, as you'll likely be asked to discuss your experience with these tools.
✨Showcase Your Problem-Solving Skills
Prepare to discuss specific examples where you've tackled complex data problems. Think about how you’ve designed scalable data ecosystems or optimised pipelines in the past. This is your chance to demonstrate your hands-on experience and how you can apply it to real-world scenarios.
✨Understand the Business Context
Familiarise yourself with how data engineering impacts business outcomes. Be ready to explain how your technical decisions can translate into value for clients. This will show that you’re not just a techie but also someone who understands the bigger picture.
✨Ask Insightful Questions
Prepare thoughtful questions about the company’s projects, team dynamics, and their approach to data governance. This not only shows your interest in the role but also helps you gauge if the company aligns with your values and career goals.
