Databricks Data Engineer (Data & AI)

Join us. But be you. At Deloitte, you’ll do more than just join something, you’ll add something. You’ve found a place that values your curiosity, passion, and desire to work alongside brilliant people on the things that really matter. There really is only one question: What impact do YOU want to make?

We are looking for an experienced and motivated Databricks Data Engineer to join our growing AI & Data Engineering team.

This role is ideal for someone who is passionate about modern data engineering, enjoys solving complex business and technical challenges, and is keen to embrace AI-assisted engineering practices to deliver solutions faster and smarter.

As a Databricks Data Engineer in Deloitte , you will design, build, and maintain scalable data and AI solutions using Databricks. You will have the opportunity to work across the full engineering lifecycle, from data ingestion and transformation through to modelling, orchestration, deployment, optimisation, and production support as well as leverage the wider capabilities that Databricks offers with Lakebase, Unity Catalog, Databricks Apps etc.

Your AI-enabled engineering mindset will help you leverage Genie Code, GitHub Copilot, and other AI development tools to accelerate delivery while maintaining strong engineering, security, and quality standards.

AI-Assisted Engineering

- An AI-enabled engineering mindset , proactively using tools such as Genie Code, Claude, GitHub Copilot, and other AI capabilities to accelerate development, automate repetitive tasks, improve code quality, and deliver solutions more efficiently.
- Good judgement when using AI-generated outputs, with a strong focus on security, maintainability, accuracy, and engineering quality .

What Success Looks Like

You are someone who can take ownership of a data engineering problem from design through to production .

You care about building solutions that are not only functional, but also scalable, maintainable, secure, automated, and well engineered .

You are comfortable working across the broader cloud, data, analytics, and AI ecosystem and are always looking for better ways of working through reusable patterns, automation, and effective use of AI-assisted development tools.

Most importantly, you combine strong technical capability with a delivery-focused and continuous-improvement mindset .

You will have the opportunity to

If you are passionate about Databricks, modern data engineering, AI, and building Lakehouse solutions at scale , we would love to hear from you.

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, and feature-ready data models to support reporting, analytics, machine learning, and Generative AI use cases.; Develop and maintain integrations between Databricks and cloud platforms, APIs, databases, files, streaming platforms, and enterprise applications.; Implement automated CI/CD pipelines and modern DevOps practices for Databricks deployments.; Develop and orchestrate workloads using Lakeflow Jobs , Azure Data Factory, Airflow, or similar technologies.; Implement appropriate security, governance, data quality, lineage, and monitoring controls .; Optimise Databricks workloads for performance, scalability, reliability, and cost efficiency .; Troubleshoot complex platform, pipeline issues across development and production environments.; Collaborate with architects, analysts, data scientists, ML engineers, and business stakeholders to translate requirements into practical technical solutions.; Contribute to engineering standards, reusable frameworks, technical documentation, and continuous improvement initiatives.; Proactively leverage Genie Code, GitHub Copilot, and other AI-powered development tools across solution design, coding, testing, troubleshooting, code review, and documentation.; Identify opportunities to use AI and automation to reduce repetitive engineering effort and accelerate the development lifecycle.

Requirements

Strong hands-on experience with Databricks .; Experience designing and building modern Lakehouse architectures .; Strong understanding of data engineering, data modelling, data warehousing, and distributed processing concepts.; Experience building batch and/or streaming data pipelines.; Experience with orchestration technologies such as Lakeflow Jobs , Azure Data Factory, Airflow, or similar tools .; 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 are advantageous.; Familiarity with infrastructure-as-code technologies such as Terraform or similar.; Strong analytical, troubleshooting, and problem-solving skills.; Excellent communication and stakeholder engagement skills.; Work on exciting project for some of New Zealand’s best companies.; Work on modern, enterprise-scale Databricks Lakehouse platforms .; Deliver solutions across Data Engineering, Analytics, AI, Machine Learning, and Generative AI .; Solve complex, real-world data engineering challenges.; Develop experience with modern engineering and AI-assisted development practices .; Work alongside experienced data engineers, architects, platform specialists, and technology leaders.; Contribute to reusable frameworks, accelerators, and engineering practices that improve how we deliver modern data platforms.

Nice to have

Experience with Generative AI, Machine Learning, or MLOps on Databricks.; Experience with Databricks Declarative Automation Bundles; Databricks certifications such as:; Databricks Certified Data Engineer Associate; Databricks Certified Data Engineer Professional

Benefits

Flexible work arrangements – work in a way that suits you, us and our clients best; Subsidised professional qualifications and paid professional memberships; Subsidised health insurance; Income protection insurance; 22 days annual leave; Social events, social sports teams, and monthly firm drinks; A focus on wellness and well-being; Community focused volunteer days; A focus on the environment and sustainability