Enterprise Data Architect

Location: SydneyContract Duration: 6 months
A global banking and financial services organisation is seeking an experienced enterprise data engineer to establish a consolidated analytics environment and unlock self-service reporting capabilities across the division. The client operates a sophisticated data stack on Google Cloud, centred on BigQuery for data warehousing and ThoughtSpot for analytics consumption. This role addresses a critical business challenge: multiple fragmented data sources, inconsistent reporting models, and manual reporting processes that hinder scalability and business agility.
You will lead the rationalisation and consolidation of the existing data architecture into a trusted, enterprise-grade analytics layer. Success is measured by the delivery of a centralised data model, optimised query performance, reduced report refresh cycles, and the enablement of self-service analytics consumption. This is a strategic engagement that positions you at the core of the organisation's data modernisation programme and offers exposure to high-stakes financial services data engineering at scale.
Key ActivitiesData Architecture Assessment: Evaluate the existing data landscape, identify fragmentation across systems, and develop a consolidation roadmap aligned with enterprise standards.Enterprise Data Modelling: Design and implement a centralised dimensional data model that serves as the single source of truth for analytics, incorporating governance and lineage metadata.BigQuery Optimisation: Optimise SQL workloads, query performance, and data warehouse costs through advanced tuning, partitioning strategies, and cluster design on Google Cloud.Semantic Layer & BI Integration: Build curated analytics-ready datasets and semantic layers that enable seamless ThoughtSpot integration and support self-service reporting without requiring SQL expertise.Orchestration & Automation: Design and implement automated data pipelines using dbt, Airflow, or equivalent tools to eliminate manual refresh processes and improve operational reliability.Stakeholder Enablement: Work closely with business teams and BI consumers to understand dashboard requirements, validate data quality, and ensure the analytics environment meets operational and strategic needs.
Your BackgroundEssential:7+ years of hands-on enterprise data engineering experience, with a track record of designing and implementing large-scale data solutions.Advanced SQL proficiency, query optimisation expertise, and deep knowledge of data warehousing principles and dimensional modelling.Cloud data engineering experience on Google Cloud, with BigQuery expertise strongly preferred.Proven experience with dbt, Airflow (or equivalent orchestration tools), and Python for data pipeline development.Strong understanding of data governance, metadata management, data lineage, and ownership frameworks.Demonstrated experience supporting and integrating BI and reporting platforms into enterprise environments.Experience with Google Analytics 4 (GA4) - broader GA4 exposure is sufficient; hands-on experience with the GA4 BigQuery export is not required.
Desirable:Direct ThoughtSpot implementation experience, including Spotter and Sage analytics development.Background with Power BI, Tableau, or Looker in supporting analytics-driven organisations.Banking, financial services, or capital markets industry experience.AWS data engineering exposure for broader cloud infrastructure context.