Project Overview

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 Activities

• Data 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 Background

Essential:

• 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.

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.