Senior Data Engineer

Title: Senior Data Engineer

Location: Sydney Australia

Job Description:

We're on a mission to be a Force For Good, through our People, Products and Purpose at Nuix. This extends to our People. We're fiercely passionate, love working at pace, thrive in ambiguity, live, and breathe outside of the box, and above all are good humans. We're determined to make a positive difference in the world, whether through our solutions which help the top companies, governments and agencies find the truth and combat illegal activities, or through our people who care about contributing and giving back both within, and outside, of Nuix. We are a Force For Good. We're selective about who comes on board, and you should be too. But if the above sounds like a match, get in touch today and get ready for the possibility of starting a once-in-a-career journey.

Role Overview:

The Senior Data Platform Engineer will design and oversee data pipelines in Databricks on AWS, manage integrations with SaaS platforms and implement robust data quality and observability frameworks. This role ensures reliable, high-performance data delivery for enterprise analytics and AI workloads.

Purpose:

As a Senior Data Platform Engineer, your primary focus will be designing and delivering scalable, reliable data ingestion and transformation pipelines that power our analytics, reporting and AI use cases.

You will play a key role in turning raw, complex data from multiple systems into high-quality, governed, analytics-ready datasets using Databricks and modern Lakehouse practices.

This role sits within our broader Data, Analytics & AI team, where responsibilities are shared across specialized focus areas. While other team members lead platform architecture, cloud infrastructure and visualization, your core contributions will be in pipeline engineering and data transformation, the foundation that everything else relies on.

Growth & Collaboration:

While this role is not primarily responsible for platform engineering or dashboard development, experience in these areas is highly valued and will help you collaborate effectively across the team.

If you're interested in expanding your skills into these areas, you will have opportunities to learn, contribute and be mentored as part of a collaborative, cross-functional team.

Location:

This position will be based in our Sydney office. The candidate is required to attend the office a minimum of 3 days per week but may voluntarily elect to work either remotely or from the Sydney office for the remaining 2 days of the week.

Key Responsibilities

• Design, build and maintain scalable ETL/ELT pipelines that ingest, transform and deliver trusted data for analytics and AI use cases.

• Build data integrations with well-known SaaS platforms such as Salesforce, NetSuite, Jira and others.

• Implement incremental and historical data processing to ensure accurate, up-to-date data sets.

• Ensure data quality, reliability and performance across pipelines through validation, testing and continuous code optimization.

• Contribute to data governance and security by supporting data lineage, metadata management and data access controls.

• Support production operations, including monitoring, alerting and troubleshooting.

• Work with stakeholders to translate business and technical requirements into well-structure, reliable datasets.

• Share knowledge and contribute to team standards, documentation and engineering best practices.

Skills, Knowledge and Expertise

• Data Ingestion & Integration: hands-on experience building robust ingestion pipelines using tools and patterns such as Databricks Auto Loader, Lakeflow Connectors, Fivetran and/or custom API / file-based integrations.

• Core Data Engineering: strong development experience using SQL, Python and Apache Spark (PySpark) for large-scale data processing.

• Data Pipeline Orchestration: proven experience developing and operating data pipelines using Databricks Workflows & Jobs, Delta Live Tables (DLT) and/or Lakeflow Declarative Pipelines.

• Incremental Processing & Data Modelling: deep understating of incremental data loading, including Change Data Capture (CDC), MERGE operations and Slowly Changing Dimensions (SCD) in a Lakehouse environment.

• Data Transformation & Lakehouse Design: experience in designing and implementing Medallion Architecture (bronze, silver and gold) using Delta Lake.

• Data Quality, Test and Observability: experience implementing data quality checks with tools and frameworks such as DLT expectations, Great Expectations or similar, including pipeline testing and monitoring.

• Data Governance & Lineage: hands-on experience with data cataloguing, lineage and metadata management within Unity Catalog to support governance, auditing and troubleshooting.

• Performance Optimization: experience tuning Spark and Databricks workloads, including partitioning strategies, file sizing, query optimization and efficient use of Delta Lake features.

• Production Engineering Practices: experience working with code versioning (Git), peer review and promoting pipelines through development, test and production environments.

• Security & Access Control Awareness: Understanding of data access control, sensitive data handling and working with Unity Catalog in the context of governed environments.

• Stakeholder & Team Collaboration: strong communication and analytical skills working with business and technical stakeholders to gather requirements, explain data concepts and support downstream users such as analysts and dashboard developers.

Desired Expertise:

• Experience with Amazon Web Services (AWS).

• Understanding of DevOps best-practices and solutions such as: Infrastructure-as-code (Terraform); Databricks Asset Bundles; CI/CD pipelines (Jenkins).

• Familiarity with data warehousing and dimensional modelling methodologies (e.g. Kimball, facts & dimensions, star schemas, data marts).

• Basic understanding of AI & ML, including preparation of structured and unstructured data for ML use cases and AI agents.