UX Analytics Lead

📍 Job Overview

Job Title: UX Analytics Lead

Company: Capgemini

Location: Multiple locations across Poland (Kraków, Lublin, Opole, Poznań, Wrocław, Katowice, Gdańsk, Warszawa)

Job Type: Full-Time

Category: User Experience (UX) Analytics / Operations

Date Posted: 2026-06-12

Experience Level: 5+ years

Remote Status: On-site

🚀 Role Summary

• Lead the establishment and scaling of a data-informed UX practice within Group IT to enhance enterprise product decision-making.

• Transform user behavior data into actionable insights, driving evidence-based product design and user experience improvements.

• Define, standardize, and evolve key UX metrics to measure user satisfaction, engagement, and operational efficiency.

• Collaborate closely with product managers, designers, and stakeholders to translate complex data into clear, strategic recommendations.

• Drive experimentation, including A/B testing and feature validation, to optimize product performance and user adoption.

📝 Enhancement Note: This role is positioned within Group IT, indicating a focus on internal enterprise applications and systems rather than external client-facing products. The emphasis on "scaling analytics" and "best in class UX organization" suggests a strategic, foundational role focused on building processes and frameworks that will be adopted across multiple product teams. The "UX Analytics Lead" title, while focused on UX, requires strong operations and data analysis skills to implement and manage these practices.

📈 Primary Responsibilities

• Analyze quantitative user behavior data to identify friction points, usage patterns, and opportunities for UX enhancement across enterprise applications.

• Develop and implement frameworks for defining, tracking, and reporting on core UX metrics (e.g., task success, drop-off rates, engagement, efficiency).

• Structure and analyze user journeys and conversion funnels for key enterprise applications to pinpoint areas for optimization.

• Collaborate cross-functionally to combine quantitative findings with qualitative insights, providing a holistic understanding of user behavior and motivations.

• Design and support experimentation initiatives, including A/B testing and feature validation, to measure the impact of UX changes.

• Establish success criteria and robust measurement frameworks for new features and product releases, ensuring alignment with business objectives.

• Build scalable UX analytics practices by developing standardized processes, dashboards, and best practice guidelines.

• Mentor designers and researchers on leveraging data effectively within their workflows to inform design decisions.

• Communicate complex data insights clearly and concisely to diverse stakeholders, including product managers, designers, and leadership.

• Contribute to the strategic roadmap for UX analytics within Group IT, identifying opportunities for innovation and continuous improvement.

📝 Enhancement Note: The responsibilities clearly indicate a need for strong analytical rigor, process development, and cross-functional collaboration, all core tenets of operations roles. The emphasis on "scaling," "standardizing," and "frameworks" points towards a strategic operations function focused on building repeatable, efficient processes for data analysis and UX measurement.

🎓 Skills & Qualifications

Education: While not explicitly stated, a Bachelor's or Master's degree in a quantitative field such as Computer Science, Statistics, Mathematics, Human-Computer Interaction (HCI), Psychology, or a related discipline is typically expected for this level of role.

Experience:

• 5+ years of progressive experience in product analytics, quantitative UX research, or a closely related analytical field.

• Proven experience partnering closely with UX/design and product management teams in product-led development environments.

• Demonstrated success in translating complex data into actionable product or design decisions that drive measurable outcomes.

• Experience working within complex or enterprise-level environments, understanding the unique challenges and opportunities. Required Skills:

• Analytics Tools Proficiency: Hands-on experience with leading analytics platforms such as Amplitude, Mixpanel, Google Analytics, or similar user behavior analytics tools.

• Data Analysis & Querying: Strong analytical mindset with the ability to structure ambiguous problems, define meaningful metrics, and perform data querying. Working knowledge of SQL is essential; proficiency in Python or similar scripting languages for data analysis is highly beneficial.

• Experimentation Design: Experience with experimentation approaches, including the design, execution, and analysis of A/B tests and other forms of controlled experiments.

• UX Metrics Definition: Ability to define, standardize, and evolve key UX metrics (e.g., task success rates, user engagement, conversion funnels, efficiency scores) that align with product goals.

• Data Storytelling: Proven ability to translate complex quantitative data into clear, concise, and actionable insights for diverse audiences, including non-technical stakeholders.

• User Journey & Funnel Analysis: Expertise in analyzing user flows, mapping user journeys, and identifying drop-off points within complex applications.

• Collaboration & Mentorship: Strong interpersonal skills for close collaboration with UX/design and product teams, and the ability to mentor others in data utilization.

Preferred Skills:

• Experience with data visualization tools (e.g., Tableau, Power BI) for creating impactful dashboards and reports.

• Familiarity with user research methodologies, both quantitative and qualitative, to complement analytical findings.

• Understanding of statistical concepts relevant to experimental design and data analysis.

• Experience with product analytics within an IT or internal technology services organization.

📝 Enhancement Note: The combination of analytics tools, SQL/Python, and experimentation design points to a need for a technically proficient individual with a strong operational understanding of how to implement and manage data-driven processes. The "enterprise environments" experience is critical for understanding Capgemini's context.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Case Studies in Data-Driven UX: Showcase 2-3 detailed case studies demonstrating how you used quantitative data to identify UX issues and influence product design decisions. Highlight the problem, your analytical approach, the insights generated, the recommended solutions, and the measurable impact (e.g., improved task success, reduced drop-off, increased engagement).

• Metrics Framework Examples: Include examples of UX metrics frameworks you have developed or contributed to. This could involve defining key performance indicators (KPIs), establishing benchmarks, and illustrating how these metrics were used for ongoing product performance monitoring.

• Experimentation Design & Analysis: Present examples of A/B tests or other experiments you have designed and analyzed. Detail the hypothesis, methodology, statistical significance, and the resulting product or UX recommendations.

• Dashboard/Reporting Samples: Provide mock-ups or anonymized examples of dashboards or reports you have created to visualize user behavior data, track UX KPIs, and communicate insights to stakeholders. Emphasize clarity, actionability, and audience relevance.

Process Documentation:

• Workflow Design & Optimization: Evidence of creating standardized processes for data collection, analysis, and insight generation related to user behavior. This includes defining steps for identifying opportunities, formulating hypotheses, and conducting analyses.

• Implementation & Automation: Examples of how you have implemented or improved processes for deploying analytics tools, integrating data sources, or automating reporting to enhance efficiency and scalability.

• Measurement & Performance Analysis: Demonstrations of how you have established measurement frameworks to track the performance of product features, UX improvements, and overall user satisfaction over time.

📝 Enhancement Note: For a role focused on establishing and scaling analytics, a portfolio demonstrating practical application of analytical skills, process thinking, and ability to drive tangible results through data is crucial. This goes beyond just listing tool proficiency; it requires showcasing how these tools and skills were applied to solve problems and improve outcomes.

💵 Compensation & Benefits

Salary Range: Given the location in Poland (multiple cities), the experience level (5+ years), and the specialized nature of the role (UX Analytics Lead), a competitive salary is expected. Based on market research for similar roles in major Polish tech hubs like Warsaw, Krakow, and Wrocław, the estimated gross annual salary range would likely be between 200,000 PLN and 300,000 PLN. This estimate considers factors such as the demand for UX analytics expertise, the complexity of the role within a large global organization like Capgemini, and the cost of living in Poland.

Benefits:

• Continuous Learning Opportunities: Access to training programs, workshops, and resources to enhance skills in UX analytics, data science, and relevant technologies.

• Collaborative Work Environment: A dynamic and supportive team culture that fosters innovation and knowledge sharing, with opportunities to work with diverse teams globally.

• Career Growth Support: Structured career development paths, mentorship programs, and opportunities for advancement within Capgemini's extensive global network.

• Comprehensive Health & Wellness: Standard benefits likely include private healthcare (e.g., Medicover, Luxmed), potentially subsidized sports cards (e.g., Multisport), and life insurance.

• Flexible Working Arrangements: While the role is on-site, there may be some flexibility in working hours or hybrid options available depending on team and project needs.

• Employee Assistance Programs: Support services for employees facing personal or professional challenges.

Working Hours: The standard working hours are expected to be 40 hours per week, typically Monday to Friday, within core business hours. There may be opportunities for flexible scheduling to accommodate project deadlines or personal needs, provided operational requirements are met.

📝 Enhancement Note: The salary range is an estimate based on typical compensation for a Lead-level analytics role in Poland. Actual compensation will depend on the candidate's specific experience, skills, and negotiation. The benefits listed are common for large, international IT consulting firms in Poland.

🎯 Team & Company Context

🏢 Company Culture

Industry: Information Technology & Services, Consulting, Digital Engineering. Capgemini operates at the forefront of digital transformation, assisting clients globally in leveraging technology for business growth and sustainability.

Company Size: Global presence with over 336,000+ employees worldwide, indicating a large, matrixed organization with diverse opportunities and structured processes.

Founded: Capgemini was founded in 1967, signifying a long history and established presence in the IT consulting landscape, with deep expertise and a robust client base.

Team Structure:

• Group IT Focus: This role is within Group IT, responsible for internal technology solutions and infrastructure that support Capgemini's global operations and client services.

• UX Organization: The role aims to build a "best in class UX organization," suggesting a dedicated or growing focus on user experience within the IT department, likely composed of UX designers, researchers, and now analytics leads.

• Cross-Functional Collaboration: Expect close collaboration with Product Management, UX Design, Engineering, and potentially Data Engineering teams across various business units and product lines.

Methodology:

• Data-Informed Decision Making: A core tenet of this role is to shift from intuition-driven to evidence-based decision-making, emphasizing rigorous data analysis and interpretation.

• Scalable Practices: Focus on building repeatable, scalable frameworks and processes for UX analytics that can be adopted across a portfolio of enterprise applications.

• Continuous Improvement: The role is intrinsically linked to continuous improvement cycles for products and user experiences through ongoing measurement and experimentation.

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

📝 Enhancement Note: Being part of Group IT means the UX Analytics Lead will be instrumental in shaping the internal digital experience for Capgemini employees and potentially for the tools used by consultants to serve clients. The emphasis on "global standards" and "unified IT services model" suggests a need for process standardization and adherence to corporate guidelines.

📈 Career & Growth Analysis

Operations Career Level: This is a "Lead" position, indicating a senior individual contributor role with significant responsibility for establishing and driving a critical function (UX Analytics). It sits above junior analytics roles and potentially below a formal management position, focusing on subject matter expertise and strategic implementation.

Reporting Structure: The role likely reports to a Head of UX, Director of Product Analytics, or a senior IT leader responsible for product development and user experience within Group IT. The position will require influencing and collaborating with various product teams and stakeholders across the organization.

Operations Impact: The UX Analytics Lead will have a direct impact on:

• Product Adoption & Engagement: By identifying and addressing user pain points, leading to better user experiences and increased usage of enterprise applications.

• Operational Efficiency: Optimizing workflows and user journeys within internal tools can lead to time savings and increased productivity for Capgemini employees.

• Data-Informed Strategy: Providing critical data insights that shape the product roadmap and strategic direction for enterprise applications.

• Building Analytical Capabilities: Establishing best practices and mentoring others, thereby upskilling the organization in data-driven decision-making.

Growth Opportunities:

• Specialization & Expertise: Deepen expertise in advanced UX analytics techniques, experimentation, and specific analytics platforms.

• Leadership & Strategy: Transition into management roles overseeing analytics teams or take on broader strategic responsibilities for UX operations across larger portfolios.

• Cross-Functional Mobility: Opportunities to move into Product Management, Senior UX Research, or Data Science roles within Capgemini.

• Global Impact: Contribute to shaping the digital experience for a global workforce, gaining exposure to diverse business needs and international markets.

📝 Enhancement Note: The "Lead" title implies a significant degree of autonomy and responsibility for defining the approach to UX analytics, making it a pivotal role for career growth in operations and analytics.

🌐 Work Environment

Office Type: On-site roles in major Polish cities. Capgemini typically provides modern, well-equipped office spaces designed to foster collaboration and productivity.

Office Location(s): Kraków, Lublin, Opole, Poznań, Wrocław, Katowice, Gdańsk, Warszawa. These are all significant business and technology hubs in Poland, offering excellent infrastructure and accessibility.

Workspace Context:

• Collaborative Spaces: Offices are likely to include a mix of individual workstations, meeting rooms, and open collaboration areas to facilitate teamwork and brainstorming.

• Technology & Tools: Access to standard office technology, high-speed internet, and the necessary software and hardware for analytics and communication.

• Team Interaction: Opportunities for regular in-person interaction with immediate team members, cross-functional colleagues, and potentially leadership, fostering strong working relationships.

Work Schedule: The role is on-site, requiring regular attendance at one of the specified office locations. While adherence to standard working hours is expected, the dynamic nature of project work may occasionally require flexibility. The focus on data analysis and continuous improvement aligns with a structured work schedule that allows for deep analytical dives.

📝 Enhancement Note: The on-site requirement suggests a preference for in-person collaboration, which can be beneficial for a role focused on establishing new processes and building relationships across teams. It also implies a need for candidates who are comfortable working within a structured corporate office environment.

📄 Application & Portfolio Review Process

Interview Process:

1. Initial Screening: A recruiter or hiring manager will review your application and conduct a brief screening call to assess basic qualifications, experience, and cultural fit.

2. Technical/Skills Interview: Expect a deep dive into your analytical skills, tool proficiency (Amplitude, Mixpanel, GA, SQL, Python), and understanding of UX metrics and experimentation. This may involve technical questions or a live coding/analysis exercise.

3. Portfolio Review & Case Study Presentation: You will likely be asked to present 1-2 selected case studies from your portfolio. Be prepared to walk through your process, the data you used, your insights, the impact, and lessons learned. Focus on clear storytelling and demonstrating your problem-solving approach.

4. Cross-Functional Interview: An interview with potential collaborators (e.g., a Product Manager or UX Designer) to assess your ability to work effectively in a team, communicate insights, and influence decision-making.

5. Hiring Manager/Leadership Interview: A final interview to discuss your strategic vision for UX analytics, leadership potential, and alignment with Capgemini's culture and goals.

Portfolio Review Tips:

• Curate Strategically: Select case studies that best showcase your ability to drive impact through data. Prioritize examples where your analysis led to tangible improvements in user experience or business outcomes.

• Structure for Impact: For each case study, clearly define the problem, your role and approach, the data sources and methods, key insights, recommended solutions, and the measured results (quantify impact whenever possible).

• Highlight Process & Methodology: Detail how you approached the problem—your analytical framework, the tools you used, and the steps you took to derive insights.

• Showcase Communication Skills: Practice presenting your findings clearly and concisely, tailoring your language to the audience. Be prepared to answer questions about your methodology and conclusions.

• Demonstrate Scalability: If possible, include examples that show how you've built or scaled processes, suggesting your ability to establish best practices for Capgemini.

Challenge Preparation:

• Scenario-Based Problems: Be ready for hypothetical scenarios where you might be asked to approach a new product, diagnose a drop in a key metric, or design an experiment.

• Data Interpretation: Prepare to interpret sample data sets or charts and explain your findings and recommendations.

• Metric Definition: Practice defining appropriate UX metrics for different product goals or user scenarios.

• Tool Proficiency: Be ready to discuss your experience with specific analytics tools and how you leverage them for deep analysis.

📝 Enhancement Note: The emphasis on a portfolio review and case study presentation is critical for this role. Candidates should prepare thoroughly to demonstrate not just technical skills but also strategic thinking and the ability to translate data into actionable business recommendations.

🛠 Tools & Technology Stack

Primary Tools:

• User Behavior Analytics Platforms: Amplitude, Mixpanel, Google Analytics (or similar tools like Adobe Analytics, Heap). Proficiency in at least one is essential.

• Data Querying & Manipulation: SQL (mandatory), Python (highly preferred for scripting, data cleaning, and analysis).

• Experimentation Platforms: Tools for A/B testing and feature flagging (e.g., Optimizely, VWO, internal tools).

Analytics & Reporting:

• Data Visualization Tools: Tableau, Power BI, Looker, or similar, for creating dashboards and reports to communicate insights effectively.

• Spreadsheet Software: Microsoft Excel or Google Sheets for data manipulation and basic analysis.

CRM & Automation:

• While not the primary focus, familiarity with CRM systems (e.g., Salesforce) and how user behavior data might integrate with them can be beneficial for understanding the broader enterprise context.

• Understanding of data pipelines and ETL processes might be advantageous for more advanced roles or collaborations with data engineering teams.

📝 Enhancement Note: The core technical requirements revolve around user behavior analytics platforms and data querying/analysis tools. Proficiency here is non-negotiable. Familiarity with experimentation tools and data visualization platforms will significantly strengthen a candidate's profile.

👥 Team Culture & Values

Operations Values:

• Data-Driven: A strong commitment to using data and evidence to inform decisions, rather than relying solely on intuition or opinion.

• Efficiency & Scalability: A focus on building processes and systems that are efficient, repeatable, and can scale to support a large portfolio of enterprise applications.

• Collaboration: A belief in the power of cross-functional teamwork, open communication, and shared ownership to achieve better outcomes.

• Continuous Improvement: A mindset geared towards ongoing learning, iteration, and optimization of both products and internal processes.

• Impact-Oriented: A drive to deliver measurable results that contribute to business objectives, such as increased user adoption, satisfaction, and operational efficiency.

Collaboration Style:

• Partnership with Product & Design: Working closely with UX designers and product managers as a strategic partner, providing data-backed insights to guide their efforts.

• Stakeholder Management: Effectively communicating with and influencing a wide range of stakeholders across different departments and levels of the organization.

• Knowledge Sharing: Proactively sharing insights, best practices, and learnings with the wider team and organization to foster a data-informed culture.

• Feedback Integration: Openness to receiving and incorporating feedback on analytical approaches, insights, and recommendations.

📝 Enhancement Note: The company's emphasis on "responsible and diverse," "inclusive and sustainable future," and Group IT's vision of "empower the business through technology that enables efficiency, profitability, and sustainable growth" suggests a culture that values ethical practices, innovation, and business impact.

⚡ Challenges & Growth Opportunities

Challenges:

• Establishing New Practices: As this role focuses on "establishing and scaling analytics," a key challenge will be building processes and gaining buy-in in an environment that may currently be less data-mature in UX.

• Data Complexity & Integration: Enterprise environments often involve complex, fragmented data sources. Integrating and making sense of this data for UX insights can be challenging.

• Influencing Without Authority: The role requires influencing product and design decisions without direct managerial authority over those teams, necessitating strong communication and relationship-building skills.

• Balancing Breadth vs. Depth: Managing analytics across a "portfolio of enterprise products" requires balancing the need for deep analysis on critical products with providing sufficient coverage for others.

Learning & Development Opportunities:

• Advanced Analytics Techniques: Opportunity to delve deeper into predictive analytics, machine learning applications in UX, and advanced statistical modeling.

• Experimentation Strategy: Develop expertise in designing complex experimentation programs and leveraging results for product strategy.

• Leadership Development: Gain experience in mentoring, guiding teams, and contributing to strategic operational planning.

• Industry Exposure: Work with cutting-edge analytics tools and methodologies within a global consulting leader, gaining exposure to diverse industry challenges and solutions.

📝 Enhancement Note: The challenges highlight the need for a proactive, resilient individual who can navigate ambiguity and drive change within a large organization. The growth opportunities are significant for someone looking to build a career in operations and analytics leadership.

💡 Interview Preparation

Strategy Questions:

• "How would you approach establishing a UX analytics practice from the ground up within a large enterprise IT department?"

• "Describe a time you used data to identify a critical UX issue that others had missed. What was your process, and what was the outcome?"

• "How would you define and measure the success of a new enterprise feature or product?"

• "Given a scenario where a key enterprise application is seeing declining user engagement, what steps would you take to diagnose the problem using data?"

• "How do you balance the need for quick insights with the rigor required for statistically sound analysis?" Company & Culture Questions:

• "What do you know about Capgemini's approach to digital transformation and employee experience?"

• "How do you see the role of data analytics evolving in UX design over the next 3-5 years?"

• "How would you foster a data-informed culture among design and product teams who may be more accustomed to intuition-led approaches?"

• "Describe your experience mentoring or coaching junior team members on data analysis." Portfolio Presentation Strategy:

• Quantify Impact: For every case study, ensure you clearly articulate the business impact using metrics. If exact numbers are confidential, use percentages or relative improvements.

• Focus on Problem-Solution-Outcome: Structure your narrative around the problem you solved, your analytical solution, and the positive outcome achieved.

• Showcase Your Process: Be prepared to detail your analytical methodology, the specific tools you used, and why you chose those methods.

• Address Challenges: Acknowledge any challenges faced during the project and how you overcame them. This demonstrates resilience and problem-solving skills.

• Tailor to Capgemini: Research Capgemini's current initiatives and areas of focus (e.g., digital engineering, AI, sustainability) and subtly weave in how your skills align with their strategic goals.

📝 Enhancement Note: Interview preparation should focus on demonstrating strategic thinking, problem-solving capabilities, strong analytical skills, and the ability to translate complex data into actionable business recommendations, all within the context of an enterprise IT environment.

📌 Application Steps

To apply for this operations position:

• Submit your application through the official Capgemini careers portal via the provided URL.

• Portfolio Customization: Select 2-3 of your most impactful case studies that best demonstrate your experience in product analytics, quantitative UX research, and driving measurable improvements through data. Ensure these highlight your process, insights, and quantifiable results.

• Resume Optimization: Tailor your resume to highlight keywords relevant to UX Analytics, Product Analytics, quantitative research, SQL, Python, Amplitude/Mixpanel/GA, A/B testing, and enterprise environments. Quantify achievements wherever possible.

• Interview Practice: Prepare to discuss your portfolio in detail, practice answering strategy and behavioral questions, and research Capgemini's values and Group IT's mission.

• Company Research: Understand Capgemini's business, its focus areas in digital engineering and consulting, and the likely objectives of Group IT in enhancing internal enterprise applications.

⚠️ 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.