Principal Data Engineer

Simple Machines is a global, independent technology consultancy operating across Sydney, New Zealand, London, and Poland. 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™.

Simple Machines is a global, independent technology consultancy operating across Sydney, New Zealand, London, and Poland. 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™.

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.

• 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

• 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

• 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

• Process and transform large datasets using Spark and Flink
• Design systems that perform in production—not just on paper

• Work across relational, No
  
  SQL, and analytical stores (Postgres, Big Query, Snowflake, Cassandra, MongoDB)
• Optimise storage formats and access patterns (Parquet, Delta, ORC, Avro)

• Implement secure, compliant data solutions with security by design
• Embed governance without killing developer velocity

• 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

• 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

• Strong Python and SQL
• Deep experience with Spark and modern data platforms (Databricks / Snowflake)
• Solid grasp of cloud data services (AWS or GCP)

• Demonstrated ownership of large‑scale data…