Data Business Analyst

Qualifications Essential Requirements Data & Engineering Skills

• SQL:
  Advanced querying, transformation, and performance optimisation
• Python:
  Strong capability for data manipulation, analysis, and automation
• Looker / LookML:
  Hands‑on experience building LookML models and dashboards
• Power BI:
  Proficient in developing BI reports and analytics solutions
• GCP:
  Experience with Big Query and familiarity with wider GCP data tooling
• Cloud Data Warehousing:
  Knowledge of cloud‑based storage and warehousing concepts
• AI/ML Exposure:
  Basic understanding of machine learning concepts; willingness to learn Big Query ML, AutoML, etc.
• Git / Git Hub:
  Proficient in version control for code and documentation

• Semantic Modelling:
  Understanding of semantic layers, business definitions, and logical data structures
• Data Quality:
  Knowledge of data validation, cleansing techniques, and QA processes
• Business Intelligence:
  Ability to build self‑service analytics and reporting layers
• Data Analysis:
  Capable of interpreting complex datasets to produce meaningful insights

• Degree:
  Bachelor’s degree in a data‑related discipline (preferred)

The Company is committed to diversity and equality of opportunity for all and is opposed to any form of less favourable treatment or harassment on the grounds of race, religion or belief, sex, marriage and civil partnership, pregnancy and maternity, age, sexual orientation, gender reassignment or disability
This position is based in Dunton, and it is expected the successful candidate will be able to attend the Dunton Campus for typically 4 days a week and remain flexible on the days they are required to attend the office according to business requirements.
As part of our pre-employment checks process, successful candidates will be required to undergo a criminal record check. This will be conducted in line with the Rehabilitation of Offenders Act 1974 and applied only to unspent convictions.

• Support data lake ingestion from source systems in preparation for developing semantic layers
• Implement data models using various tools that provide consistent business definitions across FCE and FBG
• Create reusable data abstractions and metrics that enable self‑service analytics for business teams
• Build logical data models, in collaboration with Data Architecture, that support both current reporting needs and future AI tool development
• Ensure semantic layer implementations comply with banking regulations and data governance standards

• Develop and maintain SQL queries and Python scripts to support data flows
• Source, prepare, and validate data working closely with Data Owners, Data Stewards and Data Engineering teams
• Collaborate with Data Governance, Data Owners, and Data Stewards to ensure data quality and compliance
• Create and maintain documentation for semantic layer components and business definitions

• Work with business teams across FCE to understand their data and analytics needs
• Translate business requirements into specification documents working with Data Engineering and Architecture
• Build business cases for semantic layer investments that enable future customer AI tools
• Facilitate discussions between business stakeholders and technical teams on data solutions

• Partner with Data Governance teams to implement data quality standards and controls
• Work with Data Stewards to maintain accurate business definitions and data lineage
• Support Data Owners in ensuring semantic layer solutions meet regulatory requirements
• Maintain audit trails and compliance documentation for regulated banking environments

• Design semantic layer solutions that can support future AI‑powered customer tools
• Collaborate with product teams exploring analytics applications for business customers
• Ensure semantic models provide clean, reliable data foundations for potential machine learning applications
• Stay informed about AI developments relevant to banking and customer data applications
• Support diverse innovation initiatives to enhance customer data experience