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Simple Machines
Data Science & Analytics 45d ago

Senior Data Engineer

Simple Machines
PolandPoland
Full-time
Not Disclosed
Senior-Level

Job Description

Key Skills Required

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Python2h 41mFree Trial ✨
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SQL15 minFree Trial ✨
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SparkData EngineerAWSSnowflakeGCPDatabricks

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Who We Are

Simple Machines is a global, independent technology consultancy operating across Sydney, New Zealand, London, and Poland. 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™.

The Role

This is a hands-on senior engineering role, not an architecture-only seat and not a support function. You’ll be responsible for technical direction, platform design and architectural decision-making.

You'll design and build greenfield data platforms, real-time pipelines, and data products for clients who are serious about using data properly. You’ll work in small, high-calibre teams and operate close to both the problem and the client.

If you enjoy solving hard data problems, shaping modern architectures (data mesh, data products, contracts), and delivering real outcomes — this is your lane.

What You’ll Be Doing

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

Build Modern Data Platforms

  • 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

Develop High-Performance Pipelines

  • 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

Work at Scale

  • Process and transform large datasets using Spark and Flink
  • Design systems that perform in production - not just on paper

Own Data Storage & Performance

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

Cloud, Security & Governance

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

Consult and Influence

  • Work directly with clients to understand problems and shape solutions
  • Translate business needs into pragmatic engineering decisions
  • Act as a trusted technical advisor, not just an order taker

Technical Leadership & Quality

  • 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

What We’re Looking For

Core Engineering Strength

  • Strong Python and SQL
  • Deep experience with Spark and modern data platforms (Databricks / Snowflake)
  • Solid grasp of cloud data services (AWS or GCP)

Architecture & Design Judgement

  • 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

Data Platform Experience

  • Built and operated large-scale data pipelines in production
  • Strong data modelling capability and architectural judgement
  • Comfortable with multiple storage technologies and formats

Engineering Discipline

  • Infrastructure-as-code experience (Terraform, Pulumi)
  • CI/CD pipelines using tools like GitHub Actions, ArgoCD
  • Data testing and quality frameworks (dbt, Great Expectations, Soda)

Delivery & Consulting Mindset

  • 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

Why Simple Machines

  • You’ll work on interesting, high-impact problems
  • You’ll build modern platforms, not maintain legacy mess
  • You’ll be surrounded by senior engineers who actually know their craft
  • You’ll have autonomy, influence, and room to grow

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Simple Machines

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Simple Machines (operating under simplemachines.com, legally Simple Machines Pty Ltd) is the premier, enterprise-grade data architecture consultancy, artificial intelligence enablement specialist, and custom data platform engineering powerhouse engineered to act as the definitive, high-visibility data transformation, responsible AI orchestration, and production-ready machine learning framework layer for leading global enterprises and government organizations. Founded in 2013 and led by Chief Executive Officer Daniel Pritchard and CTO Jason Martin, the boutique technology firm completely eliminates the severe systemic friction of modern enterprise data ecosystems—where organizations struggle with fragmented data silos, broken schema pipelines, non-compliant governance systems, and high-risk AI implementations that fail to deliver real-world business value—by deploying a unified, scalable data-to-intelligence matrix. Moving far beyond traditional, passive IT staffing or rigid software development agencies, Simple Machines natively unifies complex real-time decisioning engines, end-to-end cloud platform architecture, robust data contracts, custom feature stores, and automated governance workflows into a single high-availability consulting and engineering framework. With an active international presence across key technological and business hubs, the specialist firm's elite consultants engineer solutions that automate validation, define structural data ownership, and stop data quality anomalies before they spread downstream into critical fraud models or executive reporting pipelines. Under the hood, its technology delivery framework utilizes highly scalable microservices, automated CI/CD pipeline validation checks, machine learning and deep learning pipelines, and secure cloud integrations with major hyper-scalers including Amazon Web Services (AWS) and Google Cloud Platform (GCP) to process massive enterprise telemetry with absolute processing correctness and strict compliance alignment. What sets Simple Machines apart is its uncompromising dedication to replacing fragile, reactive data cleanup with structural data predictability, upstream producer accountability, and production-ready AI foundations; by bridging the gap between performance-intensive relational software engineering and strategic enterprise digital initiatives, the corporation remains a definitive cornerstone of modern algorithmic consulting and global data infrastructure transformation.

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