data engineer in data platforms

Simple Machines is a global, independent technology consultancy that designs and builds modern data platforms, intelligent systems, and bespoke software across data engineering, software engineering, and AI. It helps enterprises, scale-ups, and government turn complex data into products, platforms, and actionable decisions.

• 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 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 production-ready systems;
• Work across relational, NoSQL, and analytical data stores;
• Optimise storage formats and access patterns;
• Implement secure, compliant data solutions with security by design;
• Embed 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;
• 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.

• Strong Python and SQL skills;
• Deep experience with Spark and modern data platforms such as Databricks and Snowflake;
• Solid understanding of cloud data services in AWS or GCP;
• Demonstrated ownership of large-scale data platform architectures;
• Strong data modelling and architectural decision-making skills;
• Ability to balance performance, cost, and complexity trade-offs;
• Experience building and operating large-scale data pipelines in production;
• Experience with multiple storage technologies and formats;
• Infrastructure-as-code experience with Terraform or Pulumi;
• Experience with 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;
• Strong consulting instincts and ability to challenge assumptions and guide clients toward better outcomes;
• Ability to mentor senior engineers and influence technical culture.

• No conditions specified