Data Design Manager

📍 Job Overview

Job Title: Data Design Manager

Company: Kingfisher

Location: Krakow, Lesser Poland Voivodeship, Poland

Job Type: Full-time

Category: Data & Analytics / Management & Leadership

Date Posted: 2026-04-27

Experience Level: Mid-Senior Level (5-10 years estimated)

Remote Status: Hybrid

🚀 Role Summary

• Lead and develop a team of Data Designers, Senior Designers, and Lead Designers to produce high-quality data warehouse designs and associated documentation.

• Manage the recruitment, in-role development, and career planning for the local design team.

• Ensure adherence to agreed principles and processes for data warehouse design and documentation delivery.

• Drive improvements in ways of working, documentation templates, and overall team efficiency.

• Contribute to data warehouse design tasks as capacity allows, applying standard Data Design processes.

📝 Enhancement Note: The "Data Design Manager" title, combined with responsibilities for leading designers, data warehouse design, and data modelling, strongly suggests a role within a Data Engineering or Data Architecture function, with a focus on the foundational design aspects of data solutions. The mention of "retail business data and processes" as desirable experience further contextualizes this within a large retail organization. The AI-estimated experience level of 5-10 years is appropriate for a management role overseeing a team of designers and requiring significant technical and leadership experience.

📈 Primary Responsibilities

• Provide direct line management and career development support for a team of local Data Designers across various workstreams.

• Guide and mentor the team in creating robust data warehouse designs that meet or exceed business and technical requirements.

• Foster effective collaboration between Data Designers, stakeholders, subject matter experts, and internal teams to ensure design alignment and buy-in.

• Oversee the recruitment process for new Data Designers, ensuring the team is adequately staffed with skilled professionals.

• Champion and actively contribute to the continuous improvement of design methodologies, tools, and documentation standards.

• Execute data warehouse design tasks, adhering to established processes and best practices, when team capacity permits.

• Ensure the timely and accurate delivery of all design documentation, meeting quality standards and stakeholder expectations.

• Actively participate in cross-functional initiatives to integrate data design considerations into broader project lifecycles.

• Maintain a strong understanding of evolving data technologies and methodologies to guide the team's design approaches.

📝 Enhancement Note: The responsibilities are structured to emphasize both people management and technical oversight, which is typical for a manager role in a data organization. The inclusion of "cross-workstream local Data Designers" implies managing a team that supports multiple business units or project streams within the local geography, requiring strong organizational and prioritization skills.

🎓 Skills & Qualifications

Education: While not explicitly stated, a Bachelor's or Master's degree in Computer Science, Information Technology, Data Science, Engineering, or a related field is typically expected for roles of this nature.

Experience:

• Minimum of 2 years of experience as a dedicated Data Modeler, OR

• Minimum of 5 years of experience working with data within data warehouses.

Required Skills:

• Team Leadership & Management: Demonstrated ability to lead, motivate, and develop a team of technical professionals, including performance management and career planning.

• Data Warehouse Design: Solid understanding and practical experience in designing data warehouse structures (e.g., dimensional modeling, data vaults).

• Data Modelling: Proficiency in data modeling techniques and best practices for relational and dimensional databases.

• SQL Proficiency: Intermediate-level SQL querying skills for data analysis, validation, and design exploration.

• Mentoring & Coaching: Experience in guiding and developing colleagues, fostering skill growth and knowledge transfer.

• Stakeholder Management: Ability to effectively communicate and collaborate with diverse stakeholders, eliciting requirements and building trust.

• Communication Skills: Excellent verbal and written communication, with the ability to articulate technical concepts clearly and persuasively.

• Problem-Solving & Critical Thinking: Capacity to constructively challenge existing approaches and propose innovative alternative perspectives.

• Attention to Detail: Meticulous approach to ensuring accuracy and quality in designs and documentation.

• English Fluency: Complete fluency in both written and spoken English.

Preferred Skills:

• Databricks Experience: Experience working with data within Databricks environments, including its data warehousing capabilities.

• Retail Business Acumen: Knowledge of retail industry data and common business processes (e.g., sales, inventory, customer, supply chain).

• Data Architecture Principles: Understanding of broader data architecture concepts and best practices.

• Agile Methodologies: Familiarity with Agile development practices and how data design integrates into such frameworks.

📝 Enhancement Note: The distinction between required and desirable experience is clear. The "Intermediate-level SQL querying" is a crucial technical baseline for anyone working with data structures. The "2 years experience as a data modeller or 5 years experience of working with data in data warehouses" provides a clear pathway for candidates to qualify. The emphasis on "constructively challenge decisions" highlights a need for proactive and analytical thinking beyond just execution.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Data Warehouse Design Examples: Showcase of previous data warehouse designs, preferably illustrating dimensional modelling or other relevant data architecture patterns. This should include schema diagrams, entity-relationship diagrams (ERDs), or data vault models.

• Data Model Documentation: Evidence of creating comprehensive documentation for data models, including data dictionaries, business glossaries, and metadata management samples.

• Process Improvement Case Studies: Examples of how you have identified and implemented improvements to data design processes, workflow efficiency, or documentation standards within a team setting.

• Stakeholder Collaboration Evidence: Demonstrations of how you have effectively engaged with stakeholders to gather requirements, validate designs, and gain buy-in for data solutions.

Process Documentation:

• Workflow Design & Optimization: Examples of designing and documenting data workflows, ETL/ELT processes, or data integration pipelines, highlighting efficiency gains or improvements.

• System Implementation Standards: Documentation of standards, guidelines, or best practices followed during the implementation of data warehouse solutions.

• Performance Analysis & Metrics: Samples of how you have measured and reported on the performance or impact of data designs, potentially including metrics related to data quality, query performance, or business value realization.

📝 Enhancement Note: While the original posting doesn't explicitly mandate a portfolio, a "Data Design Manager" role, especially one focused on delivering "high-quality data warehouse designs and associated documentation," would benefit immensely from candidates who can demonstrate their capabilities through a portfolio. These requirements are inferred based on industry best practices for such roles and the need to showcase technical and leadership skills.

💵 Compensation & Benefits

Salary Range: Based on the location (Krakow, Poland), experience level (Mid-Senior/Manager), and the nature of the role (Data Design Manager in a large retail organization), a competitive salary range can be estimated. For a Data Design Manager in Krakow, with an estimated 5-10 years of experience, the gross annual salary could range from approximately 150,000 PLN to 250,000 PLN, depending on the exact experience, specific skills, and the company's compensation philosophy.

Benefits:

• Private Medical Healthcare: Comprehensive coverage through LUXMED, including specialized dental care for the employee and their family.

• Sports Card: Access to the Medicover sports card (Fit&More package) for physical well-being.

• Life Insurance: Employer-financed life insurance policy for added financial security.

• Lunch Break: A dedicated 30-minute lunch break integrated within the standard 8-hour workday.

• Professional Environment: Work in a highly professional and stimulating atmosphere conducive to growth and innovation.

• Training & Onboarding: Access to a Training & Buddy program designed for rapid adaptation and integration into the new role.

• Wellbeing Programme: Participation in a dedicated wellbeing program for employees.

• Public Transport: Co-financing for monthly public transport tickets within Krakow.

• Flexible Work: Comfortable working environment with the possibility of hybrid work arrangements (office and home office).

• Learning & Development: Opportunities for language courses, accounting courses, access to LinkedIn Learning, and co-financing for studies and certifications.

• Employee Referral Program: Incentives for referring successful candidates.

Working Hours: The standard working day is 8 hours, which, combined with the 30-minute lunch break, suggests a typical 40-hour work week. The role is advertised as hybrid, offering flexibility between office-based and remote work.

📝 Enhancement Note: The salary estimation is based on publicly available salary data for similar roles in Krakow, Poland, considering the specified experience level and the responsibilities of a Data Design Manager. This range is a guideline and actual compensation may vary. The benefits package is extensive and directly listed from the provided job description, highlighting Kingfisher's commitment to employee well-being and development.

🎯 Team & Company Context

🏢 Company Culture

Industry: Retail (Home Improvement). Kingfisher operates as one of the world's largest home improvement retailers, with a portfolio of well-known brands including B&Q, Screwfix, Castorama, Brico Dépôt, and Koçtaş. This context implies a fast-paced environment with a strong focus on customer experience, operational efficiency, and data-driven decision-making to support a broad range of retail operations.

Company Size: Large. With over 74,000 employees globally, Kingfisher is a substantial multinational corporation. This size indicates a structured organization with established processes, diverse teams, and significant opportunities for career development and impact. For operations professionals, this means working within a robust framework while potentially having the chance to influence and refine processes at scale.

Founded: Kingfisher's history dates back to 1982. This long-standing presence suggests a stable organization with deep industry knowledge and proven resilience. The company's ambition to become "the leading home improvement company" and grow the "largest community of home improvers" highlights a forward-looking strategy focused on growth and customer engagement.

Team Structure:

• Data Design Team: The Data Design Manager will lead a local team of Designers, Senior Designers, and Lead Designers. This team is likely part of a larger Data or IT department responsible for the company's data infrastructure and analytics capabilities.

• Reporting Structure: The manager will likely report to a Head of Data Architecture, Head of Data Engineering, or a similar senior data leader. The team structure emphasizes a hierarchical yet collaborative approach to data solution design.

• Cross-Functional Collaboration: Significant collaboration is expected with various business stakeholders, subject matter experts, and other technical teams (e.g., Data Engineering, BI, Analytics, IT Operations) to gather requirements and ensure designs meet business needs.

Methodology:

• Data-Driven Decision Making: Kingfisher's ambition and operational scale necessitate a strong reliance on data to inform strategy, optimize operations, and enhance customer experiences.

• Agile & Flexible Working: The company promotes flexible and agile working, indicating an adaptable approach to project delivery and team collaboration, likely incorporating elements of Agile methodologies.

• Continuous Improvement: The emphasis on encouraging new ideas, supporting experimentation, and striving for continuous improvement suggests a culture that values innovation and proactive problem-solving within its operational frameworks.

Company Website: https://www.kingfisher.com/

📝 Enhancement Note: The company context is derived from the provided "Overview" section and general knowledge of large retail organizations. The "Data Design Manager" role is positioned as a key player in enabling Kingfisher's data strategy within a specific geography, emphasizing the blend of technical expertise and leadership required.

📈 Career & Growth Analysis

Operations Career Level: This role is positioned at a Mid-Senior to Management level within the Data & Analytics domain. It requires a blend of technical data design expertise and proven people management skills. The scope includes not only individual contribution in design but also team leadership, development, and strategic input into data architecture best practices.

Reporting Structure: The Data Design Manager will report to a senior leader within the data organization (e.g., Head of Data Architecture, Director of Data Engineering). They will, in turn, manage a team of Data Designers, Senior Designers, and Lead Designers, forming a critical layer of technical leadership and execution.

Operations Impact: The Data Design Manager plays a crucial role in shaping the foundation of Kingfisher's data assets. High-quality data warehouse designs directly impact the accuracy, reliability, and performance of reporting, analytics, and business intelligence initiatives. This, in turn, influences strategic decision-making across various business functions, including sales, marketing, supply chain, and customer insights, ultimately driving operational efficiency and revenue growth.

Growth Opportunities:

• Leadership Progression: Potential to advance into more senior leadership roles such as Head of Data Architecture, Director of Data Engineering, or a broader Head of Data role, overseeing larger teams and more complex data strategies.

• Specialization & Expertise: Opportunity to deepen expertise in specific areas of data architecture, such as cloud data platforms (e.g., Databricks, Snowflake), data governance, or advanced data modeling techniques.

• Cross-Functional Mobility: Potential to move into related roles within IT or business intelligence, leveraging a strong understanding of data and its strategic application across the retail value chain.

• Mentorship & Training: Kingfisher offers various learning opportunities, including language courses, accounting courses, LinkedIn Learning access, and co-financing for studies and certifications, which can support career advancement.

📝 Enhancement Note: The analysis of career growth is based on typical progression paths for data management roles in large enterprises and the explicit mention of learning opportunities. The role's impact is framed in terms of how effective data design underpins business intelligence and strategic decision-making, a key aspect for operations professionals.

🌐 Work Environment

Office Type: Kingfisher offers a hybrid work model, blending office-based collaboration with remote work flexibility. The office environment in Krakow is described as "comfortable" and conducive to "highly professional and stimulating atmosphere."

Office Location(s): The specific office is located at Wielicka 28, Krakow, Poland. This location is likely situated in a business district, offering reasonable accessibility for local employees.

Workspace Context:

• Collaborative Environment: The hybrid model and emphasis on collaboration suggest an office designed to facilitate teamwork, meetings, and knowledge sharing among data professionals and with other departments.

• Operations Tools & Technology: Employees will have access to the necessary tools and technologies to perform their roles, including development environments, communication platforms, and potentially specialized data design software.

• Team Interaction: Opportunities for regular interaction with direct reports, peers, and senior management within the data and broader business functions, fostering a connected and informed work environment.

Work Schedule: The standard workday is 8 hours, with a 30-minute lunch break included, totaling 8.5 hours at the workplace or within working hours. This implies a 40-hour work week, with flexibility offered through the hybrid arrangement.

📝 Enhancement Note: The description of the work environment is pieced together from the "Overview" and "What's the job?" sections, focusing on the hybrid aspect, the provided office location, and the general tone of a professional, stimulating workplace. The mention of "agile working" also implies a dynamic and adaptable office culture.

📄 Application & Portfolio Review Process

Interview Process: While not explicitly detailed, a typical interview process for a Data Design Manager role at a company like Kingfisher would likely involve several stages:

• Initial Screening: HR or recruitment team review of resume and application to assess basic qualifications.

• Hiring Manager Interview: Discussion with the hiring manager to assess technical skills, leadership experience, and cultural fit. This stage may involve technical questions related to data modeling, SQL, and team management.

• Technical/Team Interview: A session with senior team members or peers to delve deeper into technical competencies, problem-solving abilities, and collaborative style. This might include a technical assessment or case study.

• Presentation/Case Study: Candidates may be asked to present a past project or a solution to a hypothetical data design challenge, demonstrating their approach, technical skills, and communication abilities.

• Final Interview: Potentially with a more senior leader (e.g., Director of Data) for final assessment and alignment.

Portfolio Review Tips:

• Curate Relevant Examples: Select 2-3 key data warehouse design projects that showcase your ability to create efficient, scalable, and well-documented data models. Prioritize projects that demonstrate your leadership in design decision-making.

• Structure Case Studies: For each project, clearly outline the business problem, your role and responsibilities, the design challenges faced, the solutions implemented (including specific modeling techniques), and the resulting business impact or benefits.

• Highlight Technical Proficiency: Be ready to walk through schema diagrams, ERDs, or data vault models, explaining design choices, trade-offs, and how they align with requirements. Demonstrate your SQL skills through examples of complex queries or data validation techniques you employed.

• Showcase Leadership & Collaboration: Emphasize instances where you mentored junior designers, collaborated with stakeholders, constructively challenged decisions, or improved team processes. Quantify achievements where possible.

• Tailor to Kingfisher: If possible, research Kingfisher's known technology stack or retail data challenges to tailor your presentation to their context.

Challenge Preparation:

• Data Modeling Scenarios: Practice designing data models for common retail scenarios (sales, inventory, customer loyalty, supply chain) under timed conditions.

• SQL Problem Solving: Prepare for questions involving complex joins, aggregations, window functions, and performance optimization of SQL queries.

• Leadership & Team Management Scenarios: Be ready to discuss how you would handle team conflicts, motivate underperformers, recruit new talent, and foster a collaborative design culture.

• Process Improvement Thinking: Prepare to articulate how you would approach improving the data design process within a large organization, focusing on efficiency, quality, and stakeholder alignment.

📝 Enhancement Note: This section infers a typical interview process for a management role in a data-focused department. The emphasis on portfolio review and case study preparation is crucial for assessing both technical and leadership capabilities for a Data Design Manager.

🛠 Tools & Technology Stack

Primary Tools:

• Data Warehouse Design Tools: While not specified, common tools include ER/Studio, Erwin Data Modeler, SQL Developer Data Modeler, Lucidchart, draw.io, or similar diagramming and modeling software.

• SQL Development Environments: Tools like SQL Server Management Studio (SSMS), Azure Data Studio, DBeaver, or specific IDEs for interacting with databases.

• Version Control: Git (e.g., GitHub, GitLab, Bitbucket) for managing design documentation and potentially DDL scripts.

Analytics & Reporting:

• BI Tools: Experience with tools like Tableau, Power BI, or Qlik Sense might be beneficial for understanding how data designs are consumed, though not a primary requirement for this role.

• Data Catalog/Metadata Management Tools: Potentially used for documenting and governing data assets.

CRM & Automation:

• CRM Systems (e.g., Salesforce): Understanding how CRM data might integrate into a data warehouse is advantageous, especially in retail.

• ETL/ELT Tools: Familiarity with tools like Informatica, Talend, SSIS, Azure Data Factory, or AWS Glue is beneficial for understanding data integration processes.

Cloud & Data Platform Technologies (Desirable):

• Databricks: Explicitly mentioned as a desirable skill, indicating Kingfisher likely leverages Databricks for its data processing and warehousing needs.

• Cloud Data Warehouses: Experience with cloud-based platforms such as Snowflake, Google BigQuery, or Amazon Redshift would be highly relevant.

📝 Enhancement Note: The tools and technology stack are inferred based on the job title, responsibilities (data warehouse design, SQL, Databricks), and common practices in large retail organizations. The emphasis is on tools directly related to data design and modeling, as well as adjacent technologies that provide context.

👥 Team Culture & Values

Operations Values:

• Data-Driven Excellence: A commitment to building robust, accurate, and performant data solutions that enable informed business decisions and drive operational efficiency.

• Collaboration & Partnership: Fostering strong working relationships with stakeholders across the business and within the data team to ensure designs meet diverse needs and are well-understood.

• Innovation & Continuous Improvement: Encouraging experimentation with new design techniques, technologies, and processes to enhance data quality, performance, and team productivity.

• Quality & Precision: A meticulous approach to data modeling and documentation, ensuring high standards of accuracy, consistency, and adherence to best practices.

• Agility & Adaptability: Embracing flexible working and agile methodologies to respond effectively to evolving business requirements and project demands.

Collaboration Style:

• Cross-Functional Integration: Proactive engagement with business units, IT, and analytics teams to translate requirements into effective data designs and ensure alignment throughout the project lifecycle.

• Knowledge Sharing: Fostering an environment where team members share best practices, design patterns, and lessons learned to elevate the collective expertise of the data design function.

• Constructive Feedback Loop: Encouraging open communication and constructive feedback within the team and with stakeholders to refine designs and processes continuously.

📝 Enhancement Note: These values and collaboration styles are inferred from the company's stated ambitions ("leading home improvement company," "grow the largest community"), emphasis on "flexible and agile working," and the role’s need to collaborate across multiple teams.

⚡ Challenges & Growth Opportunities

Challenges:

• Managing Diverse Stakeholder Needs: Balancing the varied and sometimes conflicting requirements from different business units within a large retail organization.

• Legacy Systems & Data Integration: Navigating and designing for existing legacy data systems while integrating with modern data platforms like Databricks.

• Scalability & Performance Demands: Ensuring data warehouse designs can handle the massive volumes of data generated by a global retail operation and meet stringent performance requirements for analytics.

• Team Development & Skill Gaps: Continually upskilling the design team to keep pace with evolving data technologies and methodologies, and addressing any emergent skill gaps.

• Driving Adoption of New Designs: Ensuring that new data warehouse designs are effectively adopted and utilized by downstream analytics and business teams.

Learning & Development Opportunities:

• Advanced Data Architecture: Opportunities to deepen expertise in cloud data warehousing (Databricks, Snowflake, etc.), data governance, and master data management.

• Leadership Skills: Formal and informal training in people management, strategic planning, and stakeholder engagement at a leadership level.

• Retail Data Specialization: Gaining in-depth knowledge of retail-specific data domains (e.g., supply chain optimization, customer segmentation, e-commerce analytics).

• Industry Certifications: Support for obtaining relevant certifications in data modeling, cloud platforms, or project management.

• Cross-Functional Exposure: Opportunities to work on projects that span various Kingfisher brands and geographies, broadening understanding of the global retail landscape.

📝 Enhancement Note: Challenges are identified based on the nature of managing a data design team in a large, complex, global retail environment, and common issues faced in data architecture roles. Growth opportunities are linked to the company's stated commitment to learning and development and typical career progression paths.

💡 Interview Preparation

Strategy Questions:

• Data Warehouse Design Philosophy: "Describe your approach to designing a scalable and performant data warehouse for a retail organization. What key principles do you adhere to?" (Focus on dimensional modeling, data vault, Kimball vs. Inmon, and how to adapt to retail data complexities.)

• Team Leadership & Mentoring: "How do you motivate and develop a team of data designers? Can you share an example of a time you effectively mentored a junior team member to improve their skills?" (Highlight your experience in coaching, performance management, and fostering a positive team environment.)

• Stakeholder Management & Requirements Gathering: "Imagine you have conflicting data requirements from the Sales and Marketing departments. How would you approach resolving this and ensuring the data design meets both needs?" (Demonstrate your ability to mediate, prioritize, and communicate technical trade-offs effectively.)

• Problem-Solving & Innovation: "Describe a complex data design challenge you faced and how you overcame it. What was your thought process, and what alternative solutions did you consider?" (Showcase your analytical skills, creativity, and ability to learn from challenges.)

Company & Culture Questions:

• Understanding Kingfisher's Business: "What do you know about Kingfisher's business and the home improvement retail sector? How do you see data design contributing to its success?" (Research the company's brands, market position, and strategic goals. Connect your role to business outcomes.)

• Agile & Hybrid Work: "How do you ensure effective collaboration and productivity within a hybrid work environment and when working with agile methodologies?" (Emphasize communication tools, process adherence, and maintaining team cohesion.)

• Data-Driven Culture: "How do you foster a data-driven culture within a team and encourage the adoption of new data designs by business users?" (Discuss communication strategies, training, and demonstrating the value of data.)

Portfolio Presentation Strategy:

• Structure Your Narrative: For each case study, follow a STAR (Situation, Task, Action, Result) or similar framework. Clearly define the problem, your specific role and actions, and the quantifiable outcomes or impact.

• Visualize Your Designs: Be prepared to walk through diagrams (ERDs, Dimensional Models, etc.) and explain your design choices, trade-offs, and how they address business needs. Use clear and concise language.

• Demonstrate Technical Depth: Be ready to discuss SQL querying techniques, data modeling best practices, and any specific technologies used (especially Databricks).

• Highlight Leadership Impact: Showcase instances where you led a team, mentored individuals, improved processes, or influenced design decisions. Quantify team achievements where possible.

• Engage and Interact: Treat the portfolio presentation as a conversation. Be open to questions and tailor your explanations to the interviewer's level of technical understanding.

📝 Enhancement Note: Interview preparation focuses on anticipating questions related to data design, leadership, stakeholder management, and understanding of the retail context. The portfolio presentation advice is tailored to showcasing technical expertise and leadership effectively.

📌 Application Steps

To apply for this Data Design Manager position:

• Submit your application through the provided link on the Kingfisher careers portal.

• Tailor Your Resume: Highlight your experience in team leadership, data warehouse design, data modeling, SQL, and any relevant retail or Databricks experience. Use keywords from the job description to ensure ATS compatibility.

• Prepare Your Portfolio: Curate 2-3 of your strongest data design projects that demonstrate your technical skills, leadership capabilities, and ability to deliver high-quality documentation. Be ready to present these in detail.

• Research Kingfisher: Familiarize yourself with Kingfisher's brands, business strategy, and the home improvement retail industry. Understand how data plays a role in their operations.

• Practice Interview Questions: Prepare thoughtful answers to potential interview questions, focusing on articulating your experience, leadership style, and problem-solving approach, with specific examples.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and operations industry-standard assumptions. All details should be verified directly with the hiring organization before making application decisions.