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About the Role
This is a hands-on principal engineering role focused on technical direction, platform design, and architectural decision-making. You will design and build greenfield data platforms, real-time pipelines, and data products for clients, working in small, high-calibre teams close to the problem and the client.
Responsibilities
- 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
- 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
- Work directly with clients to understand problems and shape solutions
- Translate business needs into pragmatic engineering decisions
- Act as a trusted technical advisor
- 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
Requirements
- 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 platform architectures
- Strong data modelling skills and architectural decision-making ability
- Comfortable balancing trade-offs between performance, cost, and complexity
- Built and operated large-scale data pipelines in production
- Strong data modelling capability and architectural judgement
- Comfortable with multiple storage technologies and formats
- Infrastructure-as-code experience (Terraform, Pulumi)
- CI/CD pipelines using tools like GitHub Actions, ArgoCD
- Data testing and quality frameworks (dbt, Great Expectations, Soda)
- 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
Skills
- Python
- SQL
- Spark
- Databricks
- Snowflake
- AWS
- GCP
- Data Mesh
- Data Products
- Data Contracts
- Kafka
- Flink
- Kinesis
- Pub/Sub
- Airflow
- Dataflow
- Glue
- Terraform
- Pulumi
- GitHub Actions
- ArgoCD
- dbt
- Great Expectations
- Soda
Location
- Sydney
- New Zealand
- London
- Poland
- San Francisco
Work Type
- Full-time
Experience Level
- Principal
Benefits
- Amazing Darlinghurst office space
- Team lunches on Monday & Wednesdays
- Training & Conference fund
About the Company
- 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™.