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Simple Machines

Data architecture, engineering, science, and apps

Director Technology - Data Platforms

Engineering LeadershipConfirmed live in the last 24 hours
Full-timeLondon, UKNo salary listed

About the Job

Requirements

  • At least 10 years of experience across data engineering, data platforms, analytics engineering, or data architecture.
  • Proven consultancy and enterprise client-facing experience.
  • Strong capability in pre-sales, proposal development, and solution shaping.
  • Demonstrated experience architecting and delivering modern cloud-native data platforms at enterprise scale.
  • Strong experience developing solutions centred around data products and reusable enterprise data capabilities.
  • Deep understanding of data mesh principles, federated governance, and scalable domain-oriented data ownership models.
  • Experience designing platforms and architectures to support AI-native use cases, machine learning systems, agentic workflows, and intelligent software platforms.
  • Strong understanding of real-time and event-driven architectures, including streaming data systems and low-latency processing patterns.
  • Experience building platforms that support governed, discoverable, reusable, and operationalised data products across large organisations.
  • Familiarity with contemporary data stack technologies and modern engineering practices.
  • Strong understanding of platform architecture, distributed data systems, orchestration, governance, and scalable data product design.
  • Experience with technologies and ecosystems such as GCP, AWS, Azure, Databricks, Snowflake, Kafka, Spark, dbt, Kubernetes, Airflow, and ML/AI enablement, or similar modern platform tooling.
  • Strong commercial acumen and ability to engage credibly with senior technical and business stakeholders.
  • Experience leading major enterprise initiatives and high-performing engineering teams.
  • Comfort operating in a fast-moving, high-performance consultancy environment.

Responsibilities

  • Help define and evolve the company’s data platform propositions, technical direction, and strategic positioning.
  • Advise enterprise clients on modern data platform strategy, architecture, operating models, and AI readiness.
  • Support organisations as they evolve from traditional centralised data platforms toward AI-native, real-time, and data product-oriented operating models.
  • Work closely with Engagement Managers and senior leadership across major strategic initiatives.
  • Lead architecture and technical direction for complex enterprise data platform and transformation programs.
  • Act as a senior technical counterpart across strategic client engagements and partner relationships.
  • Support pre-sales, proposal development, technical solution shaping, and executive stakeholder engagement.
  • Build trusted relationships with strategic technology partners and stay current with emerging platform capabilities and market direction.
  • Support recruitment, mentoring, capability uplift, and practice development across the broader engineering organisation.
  • Lead major client initiatives across data engineering, platform modernisation, AI enablement, and operational data systems.
  • Contribute to thought leadership, market positioning, and growth initiatives across the UK and Europe.

About the Company

Simple Machines

Data architecture, engineering, science, and apps

Simple Machines is an Australian consulting firm that combines data architecture, data engineering, data science and software development to build large-scale, distributed data platforms and machine learning applications. They design end-to-end data systems that process streaming and batch data at massive scale and help clients operationalize analytics and AI in real-world apps. Unlike firms that offer only consulting or only training, they pair hands-on implementation with formal partnerships—Confluent for Kafka, Databricks for Spark, and Lightbend for Scala—across advisory, implementation, and training. Their goal is to help enterprises, governments, and global tech companies unlock the value of their data through scalable, reliable data pipelines and AI workloads.

Company size
11-50
Company stage
Not listed
Total funding
Not listed
Headquarters
Sydney NSW, Australia
Founded
2008

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