Principal Data Engineer
About the job
- Strong proficiency in Python and SQL.
- Deep experience with Spark and modern data platforms such as Databricks or Snowflake.
- Solid knowledge of cloud data services on AWS or GCP.
- Demonstrated ownership of large-scale data platform architectures.
- Strong data modelling skills and architectural decision-making ability.
- Ability to balance performance, cost, and complexity trade-offs.
- Experience building and operating large-scale data pipelines in production.
- Comfort with multiple storage technologies and formats.
- Experience with infrastructure as code, such as Terraform or Pulumi.
- Experience building CI/CD pipelines using tools such as GitHub Actions or ArgoCD.
- Experience with data testing and quality frameworks such as dbt, Great Expectations, or Soda.
- Experience in consulting or professional services environments.
- Ability to challenge assumptions and guide clients toward better outcomes.
- Ability to mentor senior engineers and influence technical culture.
- Own end-to-end architecture for modern, cloud-native data platforms.
- Design scalable data ecosystems using data mesh, data products, and data contracts.
- Make 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.
- Integrate with client systems to enable scalable, consumer-oriented data access.
- Build and optimise batch and real-time pipelines.
- Work with streaming and event-driven technologies such as Kafka, Flink, Kinesis, and Pub/Sub.
- Orchestrate workflows using Airflow, Dataflow, and Glue.
- Process and transform large datasets using Spark and Flink.
- Design systems for production performance.
- Work across relational, NoSQL, and analytical data stores, including Postgres, BigQuery, Snowflake, Cassandra, and MongoDB.
- Optimise storage formats and access patterns using Parquet, Delta, ORC, and Avro.
- Implement secure, compliant data solutions with security by design.
- Embed data governance while maintaining 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 to clients.
- Set engineering standards, patterns, and best practices across teams.
- Review designs and code, providing technical direction and mentorship.
- Improve data quality, testing, observability, and operational excellence.
About the company
Simple Machines is an Australian consultancy that fuses data architecture, data engineering, data science, and software development to create large-scale, distributed data platforms and machine learning applications. They design end-to-end data systems that handle streaming and batch data at scale, helping organizations operationalize analytics and AI in real-world settings. They differentiate themselves through enterprise-grade streaming platforms and formal partnerships with Confluent (Kafka), Databricks (Spark), and Lightbend (Scala) for consulting, training, and implementation. Their goal is to help enterprises, governments, and global technology companies unlock value from data by building scalable, reliable pipelines and AI workloads.
Company Size
11-50
Company Stage
N/A
Total Funding
N/A
Headquarters
Sydney, Australia
Founded
2008
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Simplify's Take
What believers are saying
- Simple Machines listed 19 open roles on 2026-08-07, signaling active demand.
- DataEngBytes 2025 gold sponsorship on 2025-06-12 raised visibility among Oceania data buyers.
- Simple Machines added senior leadership across ANZ, UK, and Poland during 2026 hiring.
What critics are saying
- Consulting revenue depends on billable utilization; 19 openings pressure delivery if hires lag.
- Confluent and Databricks partnerships expose Simple Machines to vendor-led services competition by 2027.
- Without proprietary products, a referral slowdown from Databricks or Confluent would crush growth.
What makes Simple Machines unique
- Simple Machines combines data architecture, engineering, software, and AI delivery across Sydney, London, Christchurch.
- Its 2026 partner stack spans Confluent, Databricks, Snowflake, AWS, Azure, GCP, and Immuta.
- Jason Martin returned as CTO on 2025-06-12, sharpening its agentic-AI positioning.
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Benefits
- Hybrid Work Options