Senior Data Architect ( Banking )

Senior Data Architect (Banking)

We are looking for a Senior Data Architect with strong banking experience to define and own customer data use cases, segmentation strategies, and analytical requirements across customer lifecycle and campaign activities.

The role focuses on translating customer and business needs into actionable data attributes and segments, defining how customer value is measured, and ensuring that use cases can be effectively implemented and measured within customer data and campaign environments.

Key Responsibilities

• Own the customer data use case library and define the commercial purpose of each use case.

• Translate business objectives into customer attributes, segments, and actionable data requirements.

• Design and define customer segmentation strategies for targeted campaigns and customer lifecycle management.

• Own use cases end to end, including churn, retention, dormancy, win-back, upsell, tariff migration, and device upgrade.

• Define customer base management strategies and identify high-value customer segments.

• Design campaign targeting criteria and ensure segments are practical and commercially actionable.

• Work with customer data and campaign management platforms to build and activate segments.

• Define measurement frameworks for campaign performance, customer response, and incremental impact.

• Design control groups and measurement approaches to evaluate campaign incrementality.

• Analyze customer behavior and identify opportunities for retention, cross-sell, upsell, and reactivation.

• Work closely with engineering and data teams to specify required customer attributes, segment logic, and data definitions.

• Challenge unnecessary segmentation and prioritize use cases that can be practically activated and measured.

• Ensure customer segments have clear business definitions, eligibility criteria, and measurable outcomes.

• Support the development of reusable customer data and campaign use cases across banking products and customer journeys.

• Translate analytical insights into clear requirements for campaign and customer data teams.

Requirements

Requirements

• Proven experience as a Data Architect, Customer Data Architect, Marketing Analytics Lead, or similar senior role within the banking sector .

• Banking experience is mandatory.

• Strong experience in customer analytics, customer segmentation, campaign analytics, or customer value management.

• Hands-on experience designing and measuring customer campaigns.

• Strong understanding of banking commercial drivers, customer lifecycle, product relationships, and customer value.

• Experience working with customer attributes, behavioral data, transactional data, and customer profiles.

• Strong SQL skills for customer data analysis, segmentation, and campaign targeting.

• Proven experience owning customer use cases end to end, from business definition through activation and measurement.

• Experience with use cases such as churn, retention, dormancy, win-back, upsell, cross-sell, product migration, and customer reactivation.

• Experience building and using customer segments within a real customer data, CRM, or campaign environment.

• Strong understanding of campaign design, targeting logic, eligibility rules, and customer selection criteria.

• Demonstrated experience measuring campaign impact using control groups, uplift, and incrementality rather than relying only on reach or engagement metrics.

• Experience with customer data platforms, CRM platforms, or campaign management platforms.

• Ability to work closely with engineering and data teams to define attributes, business rules, segment logic, and data requirements.

• Strong understanding of customer data quality and consistency across multiple banking products and customer touchpoints.

• Ability to translate business requirements into clear, technically implementable data specifications.

• Strong commercial judgment when prioritizing customer segments and use cases based on business value and feasibility.

• Ability to simplify complex customer datasets and reduce segmentation to the audiences that can actually be activated and measured.

• Strong communication skills with both business stakeholders and technical teams.