• Data Pipeline Development: Design, build, and maintain robust data ingestion pipelines for DataOS, ensuring efficient and reliable data extraction, transformation, and loading processes.
• Automated Data Ingestion: Develop and maintain integrations with various external APIs and data providers through platforms like Fivetran or custom solutions, to automate data collection for MMM.
• AI-powered Data Quality Assurance: Contribute to the development and implementation of cutting-edge, AI-driven automated data quality assurance processes for DataOS, ensuring the accuracy, completeness, and reliability of our data.
• Performance Optimization: Analyze and optimize DataOS components for performance and efficiency, contributing to low latency and high throughput.
• Collaboration: Work effectively within a cross-functional team, collaborating with product managers, data scientists, and other engineers to deliver impactful solutions.

• Software Engineer at Heart, Data Engineer by Trade: You have 2+ years of experience in data engineering, but your foundation is built on a deep understanding of software engineering principles. You're as comfortable discussing design patterns and SOLID principles as you are with data modeling and ETL processes. You are proficient in Python and SQL, don’t mind working with Typescript and Node, and have experience designing, building, and maintaining production-grade software systems in a cloud-based environment.
• Scalability Focus: You have experience working with scalable data solutions that can handle increasing volumes of data and user traffic. You understand the challenges of scaling data pipelines and have knowledge of implementing solutions to address these challenges.
• Modern Tooling: You have hands-on experience with modern data orchestration tools like Dagster, data transformation tools like dbt, and are comfortable with infrastructure-as-code tools (e.g., Pulumi or Terraform).
• Quality-Driven: You are passionate about building high-quality, maintainable software. You have experience with testing methodologies (unit testing, integration testing) and champion best practices like code reviews and CI/CD to ensure the reliability and stability of your code.
• Growth Mindset: You're eager to learn and grow, taking on new challenges and expanding your skillset.
• Proactive Problem Solver: You can identify and solve complex data engineering challenges, taking a proactive and analytical approach.

We have a hybrid work environment with teams in Sydney, Melbourne and expanding into the US. 

With a team of around 60 people we value high quality communication, strong opinions that are held loosely and people who can give and take feedback well.

Benefits

Equity (ESOP) is offered to all staff on top of their salary and we also offer additional annual leave after 1 year of tenure (from 20 to 25 days a year) that continues to grow another day each year of service until you max out at 30 days per year.