Databricks Lakehouse Engineer — AI-Driven Data Solutions

Deloittenz; Auckland; On-site; NZD 120,000 - 180,000; Full time

Job summary

Deloitte New Zealand is seeking a Databricks Data Engineer to design, build, and maintain scalable data and AI solutions on the Databricks Lakehouse platform. You will drive end-to-end data pipelines, from ingestion to modelling and production support, leveraging Lakebase, Unity Catalog, and other Databricks capabilities.

You will collaborate with architects, data scientists, ML engineers, and business stakeholders to translate requirements into robust data architectures.

Qualifications

• Strong hands-on experience with Databricks.
• Experience building batch and/or streaming data pipelines.
• Understanding of cloud data services across Azure and/or AWS.
• Experience with Git, CI/CD, and modern software engineering practices.
• Experience using Python in data engineering solutions is advantageous.
• Familiarity with infrastructure-as-code technologies such as Terraform or similar.

Responsibilities

• Design, develop, and maintain scalable data and AI solutions on Databricks.
• Build robust data ingestion, ELT/ETL, and data processing pipelines using Databricks.
• Develop high-quality and reusable transformations through Lakeflow Declarative Pipelines.
• Design and implement Lakehouse architectures.
• Build dimensional, curated data models to support reporting, analytics, ML, and Generative AI.

Skills

Data Engineering

Python

CI/CD

Communication

Problem Solving

Tools

Databricks

Lakeflow

Azure Data Factory

Airflow

Terraform

Git

Job description

Deloitte New Zealand is seeking a Databricks Data Engineer to design, build, and maintain scalable data and AI solutions on the Databricks Lakehouse platform. You will drive end-to-end data pipelines, from ingestion to modelling and production support, leveraging Lakebase, Unity Catalog, and other Databricks capabilities.

You will collaborate with architects, data scientists, ML engineers, and business stakeholders to translate requirements into robust data architectures.