AI-First Data Engineer: Build Scalable Pipelines

The GPT Group; Sydney; Hybrid; AUD 120,000 - 180,000; Full time

Benefits offered by this job

Flexible work arrangements

Parental leave benefits

Wellbeing programs

Job summary

The GPT Group is seeking a hands-on Data Engineer to join the Data & Analytics team in Sydney. You will design, build and operate data ingestion pipelines, databases and curated datasets fundamental to our data platform and AI initiatives.

You will work within a Data Engineering sub-team reporting to the Data Engineering Manager, using AI-assisted tooling and DataOps to accelerate delivery while maintaining reliability and cost efficiency across our cloud data platform.

Qualifications

• 4-5 years delivering production pipelines and data models in a data engineering role.
• Strong SQL, Python and cloud data warehouse experience (Snowflake or comparable).
• Experience with modern analytics engineering tooling and data for AI/GenAI consumption.

Responsibilities

• Build and maintain data ingestion and pipelines across structured and unstructured sources, landing, transforming and making data available reliably and at scale.
• Develop and maintain components of the data platform (Snowflake, databases and tooling) per standards.
• Monitor pipelines, investigate failures and resolve day-to-day issues.
• Contribute to analytics engineering and the semantic layer, producing curated datasets for reporting and data products.
• Build AI-facing data foundations to support data products and AI team consumption.
• Uphold engineering standards and participate in code reviews.

Skills

SQL

Python

Snowflake

Data Warehousing

DataOps

Education

Degree in a technical field

Tools

dbt

Job description

The GPT Group is seeking a hands-on Data Engineer to join the Data & Analytics team in Sydney. You will design, build and operate data ingestion pipelines, databases and curated datasets fundamental to our data platform and AI initiatives.

You will work within a Data Engineering sub-team reporting to the Data Engineering Manager, using AI-assisted tooling and DataOps to accelerate delivery while maintaining reliability and cost efficiency across our cloud data platform.