GCP Data Engineer - Real-Time & Scalable Pipelines

NCS Group Australia; City of Melbourne; On-site; AUD 120,000 - 160,000; Full time

Benefits offered by this job

Paid parental leave

Discounted health insurance

Career development programs

AI-driven learning platform

Job summary

NCS Australia is seeking a Data Engineer to design, build and scale massive data pipelines that power critical decisions and analytics. You will architect end‑to‑end data processing using BigQuery, Dataflow, Pub/Sub, and Cloud Composer, driving robust, secure and high‑throughput systems across the organization.

You will translate complex product and data science requirements into scalable solutions, collaborate with cross‑functional teams, and champion DataOps practices to improve data quality

Qualifications

• Experience building scalable data pipelines and data models.
• Strong knowledge of GCP data services and security practices.
• Ability to translate business requirements into scalable solutions.
• Experience with CI/CD for data pipelines and DatOps principles.

Responsibilities

• Design, build and maintain scalable data pipelines (ETL/ELT).
• Optimize data models in BigQuery for performance and cost.
• Collaborate with data scientists, analysts and software engineers.
• Ensure data quality, integrity and security across platforms.
• Monitor production data systems and implement CI/CD practices.

Skills

Data pipelines

CI/CD

Data quality

Collaboration

Security

Education

GCP Professional Data Engineer Certification

Tools

BigQuery

Dataflow

Cloud Composer

Pub/Sub

Kubernetes

Terraform

Python

Job description

NCS Australia is seeking a Data Engineer to design, build and scale massive data pipelines that power critical decisions and analytics. You will architect end‑to‑end data processing using BigQuery, Dataflow, Pub/Sub, and Cloud Composer, driving robust, secure and high‑throughput systems across the organization.

You will translate complex product and data science requirements into scalable solutions, collaborate with cross‑functional teams, and champion DataOps practices to improve data quality