Senior Data Platforms Engineer

2 weeks ago

Christchurch, Canterbury, New Zealand Simple Machines Full time

Position:
Senior Data Platforms Engineer

Location:
Christchurch, New Zealand

Travel:
Potential travel within the ANZ region on occasion

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.

We engineer data to life.

The Role:


The 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 utilize their data.

Within a team of top-tier engineers, the role involves developing greenfield data solutions that deliver tangible business outcomes across various environments.


Requirements:

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.
-
Database and Storage Optimization: Optimize and manage a broad array of database technologies, from traditional relational databases (e.g., PostgreSQL, MySQL) to modern NoSQL solutions (e.g., MongoDB, Cassandra). Focus on strategies that enhance data accessibility, integrity, and performance.
-
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
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Client Advisory: Provide expert advice to clients on optimal data practices that align with their business requirements and project goals.
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Training and Empowerment: Educate client teams on the latest technologies and data management strategies, enabling them to efficiently utilise and maintain the solutions we have developed.
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Professional Development: 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.

Ideal Skills and Experience
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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.
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Data Modelling Expertise: Proficient in data modelling, understanding the implications and trade-offs of various methodologies and approaches.
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Infrastructure Configuration for Data Systems: Compete

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