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Data Engineer

Private Company - Richmond, VIC

Any Industry
Source: uWorkin


Who We Are

What do Airbnb, Slack and Salesforce have in common? They use Culture Amp every day to make their workplaces better, along with over 2,500 other companies from around the globe, making up a community who stand together to improve the world of work.

It’s what makes us the world’s leading people & culture platform.

What is the opportunity?

We are searching for a Data Engineer to join our team in Australia to take our data capability to the next level, across everything from batch and stream processing to product analytics, data science, and machine learning. Your work will enable many parts of the business to better understand and serve our customers through the collection, analysis and democratisation of our data. This is a chance to have a huge impact on transforming the world of work for the better—and learn a lot in the process.

What will you bring to the Camp?

Data engineering is an emerging and rapidly changing discipline, and we recognize that every data engineer will have a unique blend of skills. We’re looking for someone with solid software engineering fundamentals and experience with cloud infrastructure, specifically in AWS. In addition, you’ll have a track record of delivery across a number of key areas of data engineering. We don’t expect you to be an expert in all of the below, but you should have deep experience in a few and a willingness to learn the others.

  • Building high-quality batch processing systems with sophisticated orchestration, quality monitoring, and awareness of data lineage
  • Stream processing at scale using tools like Storm, Flink, Kafka, and Spark Streaming
  • Data engineering on the AWS platform, including tools like Kinesis, EMR, S3, Athena, Redshift, and SageMaker.
  • Knowledge of columnar or distributed data processing systems, such as Redshift, Hive, Spark or Presto.
  • Best practice dimensional modelling techniques such as derived fact tables and type 2 slowly changing dimensions, and leveraging them in a business intelligence system such as Tableau.
  • Productionising machine learning pipelines and applications while optimizing for automation, reproducibility, and observability
  • Building out secure data environments that enable key use cases while maintaining auditable security and privacy controls, leveraging AWS IAM and network-level security

Example activities of your day to day;

  • Implement, test, automate, and deploy large scale, efficient data pipelines to deliver dependable data and insights to where they’re needed in the business
  • Ensure high quality software engineering practices are applied to every stage of the data lifecycle
  • Design and implement a secure data access architecture, providing insights to business users while maintaining data security, privacy, and sovereignty requirements
  • Collaborate with data scientists to automate training and deployment of machine learning models
  • Design and implement a system to securely and reliably send our product usage data to third-party systems such as Zendesk, Salesforce, or Amplitude
  • Apply dimensional modelling techniques, allowing our data to be interrogated by business users in a business intelligence tool such as Looker or Tableau.