Principal Data Platform Engineer
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
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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
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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
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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
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Process and transform large datasets using Spark and Flink
- Design systems that perform in production - not just on paper
Own Data Storage & Performance
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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
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Implement secure, compliant data solutions with security by design
- Embed governance without killing developer velocity
Consult and Influence
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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
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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
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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
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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
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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
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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
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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
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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.
