Jet2 Travel Technologies Pvt Ltd.
Website:
jet2traveltech.com
Job details:
About the Role
Within Jet2 Travel Technologies Data Engineering & Analytics Department, we're improving the way analytical data assets are designed, governed and delivered across the organisation.
As part of this, our Analytics Design Squads bring together Data Architecture, Data Governance, Business Analysis and domain expertise earlier in the delivery lifecycle. The aim is to shape analytical solutions clearly before development begins, so delivery teams receive well-structured, consistent and practical design artefacts.
We're looking for a Data Architect to help turn business concepts, source data understanding and analytical requirements into high-quality logical models, dimensional models, data flows, mappings and design documentation.
Role emphasis
This is a hands-on role. A significant part of the work will involve logical and dimensional modelling, SQL-based data profiling, source analysis and preparing designs that can be handed into delivery.
What You'll Be Doing
Data Modelling & Analytical Design
• Use agreed conceptual models and business definitions to produce clear, delivery-ready Logical Data Models.
• Create dimensional analytical designs using Kimball-style techniques, including facts, dimensions, measures, grain and conformed business concepts.
• Create logical models for transformation and dimensional layers, including entities, relationships, attributes and supporting definitions.
• Produce source-to-target mappings, transformation notes and supporting design documentation for analytical delivery teams.
• Work with first-party and third-party data providers to understand available data, data contracts and the most appropriate sources to support analytics use cases.
• Profile and investigate source data using SQL and other analysis techniques to understand quality, completeness, usefulness and design implications.
• Identify assumptions, risks, issues and dependencies that need to be understood before work moves into delivery.
Data Flows, Lineage & Source Understanding
• Develop Data Flow Diagrams and lineage views showing how data moves from first-party or third-party sources into the analytics platform and through analytical layers.
• Help identify the system of record or master source for key data concepts, so the right data is sourced for analytical products.
• Document how data progresses through layered or medallion-style analytics architecture, including where it is transformed, curated and consumed.
• Support longer-term reuse of business concepts and ontology-style modelling across analytics, integrations, APIs, AI and application development where appropriate.
Analytics Design Squads
• Work within cross-functional Analytics Design Squads alongside Business Analysts, Data Governance specialists, Data Owners, Data Stewards, Product Owners, engineers and domain experts.
• Support squad-aligned domains such as Commercial and Finance, Operations and HR, and Customer.
• Work to standards defined by Senior Data Architects / Senior Data Information Architects and collaborate with Solution Architects where estimates, RAID items or delivery impacts need to be understood.
• Support handover of designs into delivery planning and help resolve design questions or defects that arise during development.
Governance & Quality
• Ensure analytical designs align with agreed modelling standards, naming conventions, governance expectations and platform patterns.
• Contribute to metadata, lineage, ownership and data quality discussions as part of the design process.
• Challenge assumptions constructively where source data, definitions or mapping logic are incomplete or unclear.
• Help improve reusable modelling patterns and design approaches as the architecture function matures.
Communication & Collaboration
• Explain models, data flows and design decisions clearly to technical and non-technical colleagues.
• Work collaboratively within a squad environment, balancing good architectural discipline with practical delivery needs.
• Use clear, structured communication to support alignment with Business Analysis, Data Governance, Engineering and product teams.
Skills & Experience
Essential skills & experience Desirable experience
• Hands-on experience creating logical data models or analytical data designs. • Snowflake or another modern cloud data platform.
• Dimensional modelling experience using Kimball-style techniques, including facts, dimensions, measures and grain. • Alation or other data catalogue, metadata management or lineage tooling.
• Strong SQL skills, with experience querying data to investigate structure, quality, completeness and suitability. • Layered or medallion-style analytics architecture.
• Experience profiling and analysing data to support data model design decisions. • Data contracts, third-party data feeds, APIs, files or source system extraction patterns.
• Experience producing data flow diagrams, lineage views, source-to-target mappings or equivalent design artefacts. • Agile or Scaled Agile delivery environments.
• Understanding of data warehousing, analytical data products or reporting/insight delivery. • Interest in AI and how AI can support data, architecture, modelling and delivery practices.
• Experience working with a modern analytical platform, data • Travel, airline or retail industry experience.
warehouse or large-scale relational database environment.
• Ability to translate business requirements and conceptual designs into practical data structures.
• Clear communication skills and the ability to work effectively within a cross-functional squad.
Personal Attributes
• Practical, structured and detail-oriented.
• Curious about how data is created, moved, transformed and used.
• Comfortable investigating ambiguity and asking good questions.
• Able to challenge assumptions constructively and professionally.
• Collaborative and able to work well with business, governance and engineering colleagues.
• Pragmatic, with a good balance of delivery focus and architectural discipline.
• Interested in continuous improvement, including how AI and automation can improve architecture and modelling practices.
Success in the Role
• Create clear, accurate and useful logical models, dimensional models, mappings, data flows and lineage artefacts.
• Improve the quality and completeness of designs before work enters delivery.
• Help delivery teams understand what needs to be built and why.
• Support better governance, ownership and understanding of analytical data assets.
• Build effective
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