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: Work on impactful projects with autonomy, surrounded by expert engineers.
- Other info: Dynamic environment with opportunities for growth and influence.
- Why this job: Make a real difference by solving complex data challenges and delivering innovative solutions.
- Qualifications: Strong Python, SQL, and experience with large-scale data platforms required.
The predicted salary is between 80000 - 100000 € per year.
Who We Are
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™.
Requirements
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'll 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 employer: Simple Machines
At Simple Machines, we pride ourselves on being an exceptional employer that fosters a culture of innovation and collaboration. Located in vibrant cities like Sydney and London, we offer our employees the chance to work on high-impact projects with cutting-edge technology while enjoying a supportive environment that encourages professional growth and autonomy. With a focus on building modern data platforms and a commitment to employee development, we provide unique opportunities for our team members to thrive and make a real difference in the world of data engineering.
StudySmarter Expert Advice🤫
We think this is how you could land Principal Data Engineer
✨Tip Number 1
Network like a pro! Reach out to your connections in the industry, attend meetups, and engage with professionals on LinkedIn. We all know that sometimes it’s not just what you know, but who you know that can help you land that Principal Data Engineer role.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving data platforms and pipelines. We want to see how you’ve tackled real-world problems and delivered results. Don’t forget to share this on our website when applying!
✨Tip Number 3
Prepare for technical interviews by brushing up on your Python, SQL, and cloud services knowledge. We recommend doing mock interviews with friends or using online platforms to simulate the experience. The more comfortable you are, the better you’ll perform!
✨Tip Number 4
Be ready to discuss your architectural decisions and trade-offs during interviews. We love candidates who can articulate their thought process and demonstrate their ability to balance performance, cost, and complexity. It’s all about showing us you can think critically!
We think you need these skills to ace Principal Data Engineer
Some tips for your application 🫡
Tailor Your Application:Make sure to customise your CV and cover letter for the Principal Data Engineer role. Highlight your experience with data platforms, cloud services, and any relevant projects that showcase your skills in building modern data solutions.
Showcase Your Technical Skills:We want to see your technical prowess! Include specific examples of your work with Python, SQL, Spark, and any cloud technologies like AWS or GCP. Don’t just list them; explain how you’ve used these tools to solve real-world problems.
Demonstrate Your Problem-Solving Ability:In your application, share instances where you've tackled complex data challenges. We love candidates who can think critically and come up with innovative solutions, so don’t hold back on those success stories!
Apply Through Our Website:We encourage you to submit your application directly through our website. It’s the best way for us to receive your details and ensures you’re considered for the role. Plus, it shows 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 such as 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 designed scalable data ecosystems or optimised pipelines. Use the STAR method (Situation, Task, Action, Result) to structure your answers and highlight your impact.
✨Understand the Business Context
Research Simple Machines and their clients to understand the types of problems they solve. Be ready to discuss how you can translate business needs into technical solutions. This shows that you’re not just a techie but also someone who understands the bigger picture.
✨Demonstrate Leadership and Mentorship
Since this role involves technical leadership, be prepared to talk about your experience mentoring others and setting engineering standards. Share examples of how you've influenced technical culture in previous roles, as this will resonate well with the interviewers.
