Principal / Senior Data Engineer (Data Platforms)

Principal / Senior Data Engineer (Data Platforms)

Wellington, New Zealand

Simple Machines. Data Engineered to Life™

Simple Machines is a leading independent boutique technology firm with a global presence, including teams in London, Sydney, San Francisco, and New Zealand. We specialise in creating technology solutions at the intersection of data, AI, machine learning, data engineering, and software engineering. Our mission is to help enterprises, technology companies, and governments better connect with and understand their organisations, their people, their customers, and citizens. We are a team of creative engineers and technologists dedicated to unleashing the potential of data in new and impactful ways. We design and build bespoke data platforms and unique software products, create and deploy intelligent systems, and bring engineering expertise to life by transforming data into actionable insights and tangible outcomes.

Requirements

The Principal / Senior Data Platforms Engineer at Simple Machines is a dynamic, hands-on role focused on building real-time data pipelines and implementing data mesh architectures to enhance client data interactions. This position blends deep technical expertise in modern data engineering methods with a client-facing consulting approach, enabling clients to effectively manage and utilise their data. Within a team of top-tier engineers, the role involves developing greenfield data solutions that deliver tangible business outcomes across various environments.

Technical Responsibilities

• Developing Data Solutions : Implement and enhance data-driven solutions integrating with clients' systems using state-of-the-art tools such as Databricks, Snowflake, Google Cloud, and AWS. Embrace modern data architecture philosophies including data products, data contracts, and data mesh to ensure a decentralized and consumer-oriented approach to data management.
• Data Pipeline Development : Develop and optimise high-performance, batch and real-time data pipelines employing advanced streaming technologies like Kafka, and Flink. Utilise workflow orchestration tools such as Dataflow and Airflow.
• Big Data Processing & Analytics : Utilise big data frameworks such as Apache Spark and Apache Flink to address challenges associated with large-scale data processing and analysis. These technologies are crucial for managing vast datasets and performing complex data transformations and aggregations.
• Cloud Data Management : Implement and oversee cloud-specific data services including AWS Redshift, S3, Google BigQuery, and Google Cloud Storage. Leverage cloud architectures to improve data sharing and interoperability across different business units.
• Security and Compliance : Ensure all data practices comply with security policies and regulations, embedding security by design in the data infrastructure. Incorporate tools and methodologies recommended for data security and compliance, ensuring robust protection and governance of data assets.

Consulting Responsibilities

Ideal Skills and Experience

Professional Experience and Qualifications