Principal / Senior Data Engineer (Data Platforms) at Simple Machines

Wellington, Wellington, New Zealand -

Full Time

Start Date

Immediate

Expiry Date

02 Feb, 26

Salary

0.0

Posted On

04 Nov, 25

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Engineering, SQL, Spark, Databricks, Snowflake, AWS, Google Cloud, Kafka, Flink, Apache Spark, Data Pipeline Development, Cloud Data Management, Data Modelling, Python, CI/CD, Agile

Industry

Information Technology & Services

Description

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. 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 Client Advisory: Provide expert advice to clients on optimal data practices that align with their business requirements and project goals. Professional Development, Training & Empowerment: Keep up with the latest industry trends and technological advancements, continually upgrading skills and achieving certifications in the technologies Simple Machines implements across its client base. Educate client teams and enable their efficient utilisation and maintenance of these solutions Ideal Skills and Experience Core Data Engineering Tools & Technologies: Demonstrates proficiency in SQL and Spark, and familiarity with platforms such as Databricks and Snowflake. Well-versed in various storage technologies including AWS S3, Google Cloud BigQuery, Cassandra, MongoDB, Neo4J, and HDFS. Adept in pipeline orchestration tools like AWS Glue, Apache Airflow, and DBT, as well as streaming technologies like Kafka, AWS Kinesis, Google Cloud Pub/Sub, and Azure Event Hubs. Data Storage Expertise: Knowledgeable in data warehousing technologies like BigQuery, Snowflake, and Databricks, proficient in managing various data storage formats including Parquet, Delta, ORC, Avro, and JSON to optimise data storage and retrieval. Data Modelling Expertise: Proficient in data modelling, understanding the implications and trade-offs of various methodologies and approaches. Infrastructure Configuration for Data Systems: Competent in setting up data system infrastructures, favouring infrastructure-as-code practices using tools such as Terraform and Pulumi. Programming Languages: Proficient in Python and SQL, with additional experience in programming languages like Java, Scala, GoLang, and Rust considered advantageous. CI/CD Implementation: Knowledgeable about continuous integration and continuous deployment practices using tools like GitHub Actions and ArgoCD, enhancing software development and quality assurance. Agile Delivery and Project Management: Skilled in agile, scrum, and kanban project delivery methods, ensuring efficient and effective solution development. Consulting and Advisory Skills: Experienced in a consultancy or professional services setting, offering expert advice and crafting customised solutions that address client needs. Effective in engaging stakeholders and translating business requirements into practical data engineering strategies. Professional Experience and Qualifications Professional Experience: At least 8+ years of data engineering or equivalent experience in a commercial, enterprise, or start-up environment. Consulting experience within a technology consultancy or professional services firm is highly beneficial. Educational Background: Degree or equivalent experience in computer science or a related field. Right to Work: Must have full New Zealand working rights and reside in Wellington

Responsibilities

The role involves building real-time data pipelines and implementing data mesh architectures to enhance client data interactions. It requires developing greenfield data solutions that deliver tangible business outcomes across various environments.