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Sydney | Hybrid | Full-time

About the Role:

We’re looking for an experienced Principal Data Engineer who combines deep data engineering expertise with strong solution architecture, consulting and pre-sales capability.

This is a senior customer-facing role for someone who can lead technical discovery, understand complex business and data challenges, shape the right solution, develop estimates and proposals, and provide technical leadership through delivery.

You’ll work across Azure, AWS and GCP, helping customers design and deliver modern, scalable data platforms using technologies such as Microsoft Fabric, Databricks and Snowflake.

You’ll be equally comfortable whiteboarding an architecture with a customer, presenting a solution to senior stakeholders, and working with engineers to ensure it can be successfully delivered.

What you’ll do:

Pre-Sales & Solutioning:

Lead customer discovery, technical workshops and solution architecture sessions.
Translate business and technical requirements into pragmatic data solutions.
Develop solution architectures, implementation approaches, roadmaps and effort estimates.
Contribute to proposals, RFP/RFQ responses and Statements of Work.
Partner with Sales, Account and Delivery teams throughout the opportunity lifecycle.
Present solution options, architectural trade-offs and recommendations to senior stakeholders.
Balance technical excellence with commercial considerations including cost, scalability, delivery risk and time to value.

Data Engineering & Architecture:

Architect modern enterprise data platforms across Azure, AWS and GCP.
Design solutions using platforms such as Microsoft Fabric, Databricks, Snowflake and cloud-native data services.
Design modern Data Lake, Data Warehouse and Lakehouse architectures.
Define scalable patterns for ETL/ELT, batch and streaming, APIs, data integration and data modelling.
Incorporate security, governance, data quality, observability and performance into solution designs.
Establish engineering standards, reusable patterns and best practices.
Provide technical leadership from initial solutioning through to implementation and delivery.

Technical Leadership:

Act as a trusted technical advisor to customers.
Provide technical direction across complex data engineering engagements.
Mentor and guide engineers and technical teams.
Review solution designs and engineering approaches.
Bridge the gap between customer requirements, sales, architecture and delivery.
Ensure solutions proposed during pre-sales are practical, scalable and deliverable.

What you’ll bring:

Significant experience in Data Engineering, Data Architecture or Data Platform Engineering, ideally at Principal, Lead or Architect level.
Proven experience designing and delivering enterprise-scale data platforms.
Deep expertise in at least one major cloud platform, with broader knowledge across Microsoft Azure, AWS and GCP.
Strong experience across a combination of Microsoft Fabric, Databricks, Snowflake, Azure Data Factory / Synapse, AWS Glue / Redshift, Google BigQuery / Dataflow, Spark, Python and SQL.
Strong knowledge of Lakehouse, Data Lake, Data Warehousing, ETL/ELT, streaming, data modelling and integration patterns.
Understanding of DevOps/DataOps, CI/CD, security, data governance and data quality.
Strong customer-facing consulting and communication skills.

Pre-Sales & Consulting Experience: This is a core requirement of the role.

You’ll be comfortable:

Leading discovery and architecture workshops.
Taking ambiguous customer requirements and turning them into clear solution designs.
Developing estimates, assumptions, delivery approaches and technical roadmaps.
Contributing to commercial proposals and Statements of Work.
Explaining complex architecture in clear business language.
Presenting to CIOs, CTOs, Heads of Data, architects and engineering teams.
Challenging requirements constructively and recommending the right technology and architecture rather than simply implementing what has been requested.

AI & Modern Data Workloads:

You should have:

Working knowledge of AI and Generative AI architecture concepts.
Understanding of Retrieval-Augmented Generation (RAG) and associated enterprise data flows.
Familiarity with embeddings, vector search and unstructured data processing.
Understanding of how AI agents and copilots interact with enterprise data, APIs and systems.
Exposure to AI services within Azure, AWS or GCP.
Strong understanding of the data foundations, pipelines, governance, security and data quality required to support AI and machine learning workloads.
Ability to work effectively with AI/ML specialists when shaping broader Data & AI solutions.
Deep expertise in developing or training AI/ML models is not required.

What will make you stand out: You enjoy working across the whole solution lifecycle: Discovery → Architecture → Technology Selection → Estimation → Proposal/SOW → Customer Presentation → Technical Leadership → Delivery

You’re not someone who simply waits for requirements and builds pipelines. You enjoy getting close to the customer problem, determining what should be built, why, which technology is appropriate, what it will take to deliver, and how to make it successful.

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