Senior Data Engineer

Simple Machines is a global, independent technology consultancy operating across Sydney, New Zealand, London, and Poland. They design and build modern data platforms, intelligent systems, and bespoke software at the intersection of Data Engineering, Software Engineering, and AI. They work with enterprises, scale-ups, and government to turn messy, high-value data into products, platforms, and decisions that actually move the needle.

• Strong Python and SQL skills
• 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 modeling skills and architectural decision-making ability
• Experience building and operating large-scale data pipelines in production
• Comfortable with multiple storage technologies and formats
• Infrastructure-as-code experience (Terraform, Pulumi)
• CI/CD pipeline experience using tools like GitHub Actions, ArgoCD
• Experience with data testing and quality frameworks (dbt, Great Expectations, Soda)
• Experience in consulting or professional services
• Strong consulting instincts and ability to mentor senior engineers

Nice to have:

• Experience working with architectural patterns like data mesh, data products, and data contracts
• Experience with streaming technologies such as Kafka, Flink, Kinesis, Pub/Sub

Lead Platform & Architecture Design:

• Own end-to-end architecture of 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

Build Modern Data Platforms:

• Design and deliver cloud-native data platforms using Databricks, Snowflake, AWS, and GCP
• Apply architectural patterns: data mesh, data products, and data contracts
• Integrate with client systems for scalable data access

Develop High-Performance Pipelines:

• Build and optimize batch and real-time pipelines
• Work with streaming and event-driven technologies 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 production-quality systems

Own Data Storage & Performance:

• Work across relational, NoSQL, and analytical stores (Postgres, BigQuery, Snowflake, Cassandra, MongoDB)
• Optimize storage formats and access patterns (Parquet, Delta, ORC, Avro)

Cloud, Security & Governance:

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

Consult and Influence:

• Work directly with clients to shape solutions
• Translate business needs into engineering decisions
• Act as a trusted technical advisor

Technical Leadership & Quality:

• Set engineering standards, patterns, best practices
• Review designs and code; provide mentorship
• Raise standards on data quality, testing, observability, operational excellence