UX Analytics Lead (Python, SQL)

📍 Job Overview

Job Title: UX Analytics Lead (Python, SQL)

Company: Capgemini

Location: Multiple Locations in Poland (Opole, Warszawa, Lublin, Kraków, Poznań, Wrocław, Katowice, Gdańsk)

Job Type: Full-Time

Category: Data & Analytics / UX Research Operations

Date Posted: September 05, 2026

Experience Level: Mid-Senior Level (5+ years)

Remote Status: Hybrid

🚀 Role Summary

• Lead the establishment and scaling of UX analytics practices across a diverse portfolio of enterprise products, driving data-informed product development.

• Transform complex user behavior data into clear, actionable insights and strategic recommendations for design and product teams to enhance user experiences and achieve measurable product outcomes.

• Define, standardize, and champion key UX metrics (e.g., task success, drop-off rates, engagement, efficiency) to establish consistent performance benchmarks.

• Develop and implement scalable analytics frameworks, dashboards, and best practices, mentoring team members on effective data utilization and analysis techniques.

📝 Enhancement Note: This role is positioned at the intersection of UX, Product Management, and Data Science, focusing on leveraging quantitative and qualitative data to optimize user experiences within enterprise-level applications. The emphasis on "scaling analytics" and "establishing practices" suggests a strategic, foundational role rather than purely execution-based.

📈 Primary Responsibilities

• Analyze intricate user behavior patterns across key applications to pinpoint friction points, identify areas for user experience enhancement, and uncover strategic improvement opportunities.

• Translate raw quantitative and qualitative data into compelling narratives and actionable recommendations, clearly articulating the "what" and "why" behind user interactions to cross-functional stakeholders.

• Define, document, and standardize critical UX metrics, ensuring consistent measurement and reporting of user engagement, task completion, and overall product efficiency.

• Map and structure user journeys and conversion funnels for core applications, providing a clear visualization of user flows and potential drop-off points.

• Establish clear success criteria and KPIs for new features and product releases, enabling data-driven validation and iteration cycles.

• Design, implement, and support experimentation initiatives, including A/B testing and feature validation studies, to rigorously test hypotheses and optimize product design.

• Develop and maintain scalable analytics frameworks, comprehensive dashboards, and robust best practices to embed data-driven decision-making across product teams.

• Mentor and guide UX researchers, designers, and product managers in leveraging data analytics tools and methodologies to inform their work and improve user-centric design.

📝 Enhancement Note: The responsibilities highlight a blend of strategic planning (scaling analytics, defining metrics) and hands-on execution (analyzing behavior, designing experiments). The emphasis on "enterprise products" and "complex environments" suggests a need for candidates who can navigate large-scale, potentially intricate systems and stakeholder landscapes.

🎓 Skills & Qualifications

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

Experience: Minimum of 5 years of progressive experience in product analytics, quantitative UX research, or a closely related analytical field, with a demonstrated history of impactful contributions to product strategy and design.

Required Skills:

• Proven expertise in product analytics, with a strong ability to interpret user behavior data and derive actionable insights.

• Hands-on experience with leading UX analytics and product analytics platforms such as Amplitude, Mixpanel, Google Analytics, or similar tools.

• Proficiency in data querying languages, specifically Python and SQL, for data extraction, manipulation, and analysis.

• Demonstrated ability to define ambiguous problems, structure analytical approaches, and establish meaningful, measurable metrics.

• Solid experience in designing and executing experimentation methodologies, including A/B testing, multivariate testing, and feature validation studies.

• Experience in complex or enterprise-level environments, understanding the nuances of large-scale product ecosystems and stakeholder management.

• Strong collaboration skills, with a proven track record of partnering effectively with UX/design teams, product managers, and engineering.

• Excellent communication and presentation skills, with the ability to translate complex data findings into clear, concise, and compelling recommendations for diverse audiences. Preferred Skills:

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

• Familiarity with qualitative research methodologies to complement quantitative findings.

• Experience in mentoring junior analysts or researchers.

• Understanding of user-centered design principles and methodologies.

📝 Enhancement Note: The requirement for Python and SQL indicates a hands-on data analysis role, not just reporting. The emphasis on "structuring ambiguous problems" and "defining meaningful metrics" points to a candidate who can operate with a degree of autonomy and strategic thinking in a data-rich environment. Experience in "complex or enterprise environments" is a key differentiator for this role at Capgemini.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Demonstrate a portfolio showcasing impactful UX analytics projects, highlighting your ability to translate user behavior data into concrete product improvements and measurable business outcomes.

• Include case studies that clearly outline the problem, your analytical approach (including tools and methodologies used), key findings, and the resulting product or design changes.

• Showcase examples of metrics you have defined and tracked, illustrating your understanding of UX KPIs and their impact on product success.

• Present evidence of your experience in structuring user journeys, funnels, or complex user flows within digital products. Process Documentation:

• Provide examples or descriptions of frameworks you have developed or implemented for scaling analytics practices within product teams.

• Illustrate your approach to designing and executing A/B tests or other experimentation initiatives, including hypothesis formulation, test design, and result interpretation.

• Showcase your ability to create clear and actionable dashboards or reports that effectively communicate key UX metrics and insights to non-technical stakeholders.

📝 Enhancement Note: For a role focused on "establishing and scaling analytics," a portfolio demonstrating not just analytical skills but also the ability to build and document processes, frameworks, and reporting mechanisms will be crucial. Candidates should be prepared to discuss how they've operationalized data insights.

💵 Compensation & Benefits

Salary Range: For a UX Analytics Lead with 5+ years of experience in Poland, particularly in major tech hubs like Kraków, Wrocław, or Warsaw, a competitive annual gross salary range would typically be between 150,000 PLN and 250,000 PLN. This estimate accounts for the specialized skills in Python, SQL, UX analytics tools, and the leadership/mentoring aspect of the role.

Benefits:

• Well-being Focus: Comprehensive medical care (Medicover), private life insurance, and a Sports card.

• Mental Health Support: Access to the Capgemini Helpline for therapeutic support and an educational podcast on well-being.

• Learning & Development: Extensive access to over 70 training tracks with certification opportunities (e.g., GenAI, Excel, Business Analysis, Project Management) on the NEXT platform. Free access to Education First languages, TED Talks, and Udemy Business materials.

• Performance Management: Continuous feedback and transparent performance discussions facilitated by the GetSuccess tool.

• Hybrid Working Model: Flexible work arrangement combining office presence with remote work after onboarding, supported by a home office package (laptop, monitor, chair).

Working Hours: Standard full-time working hours are assumed to be approximately 40 hours per week, with the flexibility offered by the hybrid model allowing for effective work-life integration.

📝 Enhancement Note: The salary range is an estimate based on Polish market data for similar roles requiring advanced analytics skills, Python/SQL proficiency, and leadership experience in the IT/consulting sector. Actual compensation will vary based on specific experience, negotiation, and final role scope. The benefits package is robust, emphasizing employee well-being and continuous professional development, which are strong selling points.

🎯 Team & Company Context

🏢 Company Culture

Industry: Technology and Consulting Services. Capgemini is a global leader in providing AI-powered business and technology transformation services, working with a wide range of industries including financial services, consumer products, public sector, and manufacturing.

Company Size: Over 420,000 employees globally, indicating a large, established organization with extensive resources and a broad market reach. This size offers opportunities for diverse career paths and exposure to various projects and technologies.

Founded: Capgemini has a heritage of nearly 60 years, signifying stability, deep industry experience, and a well-established reputation in the consulting and technology landscape.

Team Structure:

• This role is part of a growing "best-in-class UX organization" within Capgemini, suggesting a dedicated focus on user experience design and research.

• The UX Analytics Lead will likely work within a matrixed structure, collaborating closely with UX/design teams, product managers, and engineering teams across various enterprise product portfolios.

• The role involves mentoring researchers and designers, implying a leadership or senior individual contributor role with influence over junior team members. Methodology:

• Capgemini emphasizes an AI-powered approach to transformation, suggesting a strong focus on data science, machine learning, and advanced analytics in their service delivery.

• The company promotes a data-driven approach to decision-making, aligning with the core function of this UX Analytics Lead role.

• Expect a methodology that balances structured processes (frameworks, metrics, standardization) with agile experimentation and continuous improvement.

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

📝 Enhancement Note: Capgemini's global presence and diverse client base mean this role offers exposure to a wide array of business challenges and technological solutions. The company's commitment to diversity and inclusion is a significant cultural aspect for potential employees.

📈 Career & Growth Analysis

Operations Career Level: This role is classified as a Lead position, indicating a senior individual contributor or a first-level management role focused on a specialized area (UX Analytics). It requires a deep understanding of analytics principles, proficiency in relevant tools and languages, and the ability to strategize, implement, and mentor. It sits above a standard Analyst or Researcher role and below a Director or Head of Analytics.

Reporting Structure: The UX Analytics Lead will likely report to a Head of UX, Director of Product, or a senior leader within the analytics or digital transformation division. They will collaborate closely with product teams, design leads, and potentially data engineering or data science departments.

Operations Impact: The UX Analytics Lead has a direct impact on revenue and business decisions by:

• Identifying opportunities to improve user conversion rates, engagement, and retention through data-driven insights.

• Reducing product development risks by providing evidence-based validation for design and feature decisions.

• Optimizing user experience, leading to increased customer satisfaction and loyalty, which indirectly impacts long-term revenue and market share.

• Ensuring product features meet user needs and business objectives, contributing to successful product launches and adoption. Growth Opportunities:

• Specialization: Deepen expertise in specific areas of UX analytics, such as experimentation, personalization, or AI-driven user insights.

• Leadership: Progress into management roles, leading larger analytics teams or taking on broader strategic responsibilities for analytics across multiple product lines or business units.

• Cross-functional Expertise: Develop broader skills in product management, UX strategy, or data science, leveraging the diverse projects within Capgemini.

• Industry Exposure: Gain experience across various client industries through Capgemini's consulting engagements, broadening professional perspective and market knowledge.

📝 Enhancement Note: The "Lead" title suggests significant autonomy and a mandate to build and influence. Growth opportunities are likely tied to demonstrating success in scaling practices and driving measurable impact, leading to more strategic roles or leadership positions within Capgemini's growing UX and data practice.

🌐 Work Environment

Office Type: Capgemini offers a hybrid working model. This means employees will have access to modern office spaces while also having the flexibility to work remotely. The offices are designed to facilitate collaboration and innovation.

Office Location(s): The role is open to multiple locations across Poland, including Opole, Warszawa, Lublin, Kraków, Poznań, Wrocław, Katowice, and Gdańsk. This provides significant geographical flexibility for candidates within Poland.

Workspace Context:

• The office environment is expected to be collaborative, supporting cross-functional teamwork essential for UX and product development.

• Access to necessary tools and technology, including a home office package (laptop, monitor, chair), will be provided to support the hybrid work model.

• Opportunities will exist for direct interaction with UX designers, product managers, researchers, and other analytics professionals, fostering knowledge sharing and professional development.

Work Schedule: While a standard 40-hour work week is typical, the hybrid model and Capgemini's focus on well-being suggest a degree of flexibility in managing work hours to accommodate personal needs and project demands, particularly for deep analytical work.

📝 Enhancement Note: The hybrid model, combined with a wide selection of office locations, offers a strong work-life balance proposition. The emphasis on modern office spaces and collaboration tools indicates a commitment to providing a productive and engaging work environment for employees.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: A recruiter will likely conduct an initial call to assess your overall fit, experience, and salary expectations. Be prepared to articulate your career trajectory and interest in UX analytics.

• Hiring Manager Interview: A discussion with the hiring manager to delve deeper into your experience with Python, SQL, analytics tools (Amplitude, Mixpanel), and your approach to defining metrics and structuring user journeys. Expect questions about past projects and challenges.

• Technical/Case Study Assessment: This is a critical stage. You will likely be given a problem or dataset and asked to perform an analysis, define metrics, or propose an experimentation strategy. This could be a take-home assignment or a live coding/problem-solving session. Be ready to showcase your analytical process and ability to derive actionable insights.

• Cross-functional/Team Interview: An opportunity to meet with potential peers (UX researchers, designers, product managers) to assess collaboration style, communication skills, and cultural fit.

• Final Interview: Potentially with a senior leader to discuss strategic vision, leadership potential, and overall alignment with Capgemini's goals.

Portfolio Review Tips:

• Curate Selectively: Choose 2-3 of your most impactful UX analytics projects that demonstrate a range of skills (data analysis, metric definition, experimentation, stakeholder communication).

• Structure Your Case Studies: For each project, clearly define the business problem, your role and responsibilities, the data and tools used (Python, SQL, Amplitude, etc.), your methodology, key findings, actionable recommendations, and most importantly, the measurable impact achieved.

• Quantify Impact: Whenever possible, use numbers and metrics to demonstrate the success of your work (e.g., "increased conversion by X%", "reduced drop-off by Y%", "informed design decision leading to Z improvement").

• Highlight Process: Explain how you approached the problem, not just what you did. Discuss your thought process, challenges encountered, and how you overcame them.

• Prepare for Discussion: Be ready to walk through your portfolio examples in detail, answer probing questions about your methodology, and defend your insights and recommendations.

Challenge Preparation:

• Practice SQL & Python: Brush up on your SQL query writing for common analytical tasks (joins, aggregations, window functions) and Python for data manipulation (Pandas) and basic analysis.

• UX Metrics Knowledge: Be familiar with common UX metrics (e.g., SUS, NPS, task success rate, completion time, error rate, conversion rates, retention rates) and when to apply them.

• Experimentation Design: Understand the principles of A/B testing, hypothesis formulation, sample size calculation, and statistical significance.

• Problem Decomposition: Practice breaking down complex, ambiguous problems into smaller, manageable analytical tasks.

• Storytelling: Develop a narrative structure for presenting your analytical findings that clearly links data to business value and actionable insights.

📝 Enhancement Note: The interview process strongly emphasizes practical application of skills, particularly through a case study or take-home assignment. A well-prepared portfolio that clearly demonstrates impact and process is non-negotiable for this role.

🛠 Tools & Technology Stack

Primary Tools:

• Analytics Platforms: Amplitude, Mixpanel, Google Analytics (or similar product analytics tools). Proficiency in at least one is essential, with experience in multiple being a strong advantage.

• Data Querying & Analysis: Python (with libraries like Pandas, NumPy) and SQL are mandatory for data extraction, manipulation, and in-depth analysis.

• Experimentation Platforms: Experience with A/B testing tools (e.g., Optimizely, VWO, or built-in platform capabilities) for designing and analyzing experiments.

Analytics & Reporting:

• Data Visualization Tools: While not explicitly listed as required, familiarity with tools like Tableau, Power BI, or Looker for creating dashboards and reports would be beneficial for communicating insights effectively.

• Spreadsheet Software: Advanced proficiency in Excel or Google Sheets for data analysis and reporting.

CRM & Automation:

• While not a primary focus of this role, an understanding of how UX analytics integrates with CRM data (e.g., Salesforce) or marketing automation platforms can be advantageous for a holistic view of the customer journey.

• Familiarity with ticketing systems or project management tools (e.g., Jira) for collaboration and task tracking.

📝 Enhancement Note: The core technical requirements revolve around product analytics platforms and data manipulation/querying skills (Python, SQL). Candidates should be prepared to discuss their specific experience and comfort level with these technologies and how they've used them to drive product decisions.

👥 Team Culture & Values

Operations Values:

• Data-Driven Decision Making: A strong emphasis on using data, not just intuition, to guide product development and strategy. This role is central to embedding this value.

• Customer Centricity: A commitment to understanding and improving the user experience, ensuring that user needs are at the forefront of product design and iteration.

• Collaboration & Cross-Functional Partnership: The company values teamwork and the ability to work effectively with diverse teams (UX, product, engineering, business).

• Innovation & Continuous Improvement: A culture that encourages experimentation, learning from data, and constantly seeking ways to optimize processes and products.

• Accountability & Ownership: Taking responsibility for outcomes and driving projects to completion, with a focus on delivering measurable business value.

Collaboration Style:

• Partnership: The role requires close collaboration with UX/design and product teams, acting as a strategic partner rather than just a data provider.

• Influence: The ability to influence decision-making through clear, data-backed insights and compelling communication is key.

• Mentorship: A willingness to share knowledge and mentor others in data analytics practices, fostering a data-literate culture.

• Feedback Culture: An environment where constructive feedback is welcomed and utilized for continuous improvement of both products and processes.

📝 Enhancement Note: Capgemini's emphasis on diversity, inclusion, and well-being suggests a supportive and professional work environment. The operations values align directly with the core responsibilities of a UX Analytics Lead, highlighting the importance of data, user focus, and collaborative problem-solving.

⚡ Challenges & Growth Opportunities

Challenges:

• Scaling Analytics: Establishing and scaling consistent, high-quality UX analytics practices across a portfolio of diverse enterprise products presents a significant challenge, requiring robust frameworks and stakeholder buy-in.

• Data Integration: Integrating data from various sources and ensuring data quality and consistency across different applications can be complex in enterprise environments.

• Ambiguity: Dealing with ambiguous problems and defining clear, measurable goals in a fast-paced product development cycle requires strong analytical and problem-solving skills.

• Stakeholder Management: Effectively communicating complex findings and influencing decision-makers across different departments and levels of technical understanding.

• Balancing Quantitative & Qualitative: Integrating quantitative data with qualitative insights to provide a holistic understanding of user behavior can be challenging but is crucial for this role.

Learning & Development Opportunities:

• Advanced Analytics Techniques: Opportunities to explore and implement more advanced analytical methods, potentially including predictive modeling, machine learning applications in UX, or advanced experimentation designs.

• Leadership Development: Potential to grow into a management role, leading a team of UX analysts, or taking on broader strategic responsibilities for analytics within Capgemini.

• Industry Exposure: Working with a wide range of clients and industries through Capgemini's consulting arm provides broad exposure to different business challenges and technological solutions.

• Skill Enhancement: Access to Capgemini's extensive training resources (NEXT platform, Udemy Business, etc.) allows for continuous upskilling in areas like AI, GenAI, project management, and more.

📝 Enhancement Note: The challenges are inherent to a senior role focused on building and scaling functions. They also present significant opportunities for professional growth, particularly for individuals who thrive in complex, data-driven environments and enjoy influencing strategic decisions.

💡 Interview Preparation

Strategy Questions:

• "Describe a time you identified a significant user behavior pattern that led to a major product change. What was your process, what tools did you use, and what was the impact?" (Focus on problem definition, analytical approach, tools, and quantifiable results.)

• "How would you approach defining and standardizing UX metrics for a new enterprise application with limited historical data?" (Assess your ability to structure problems and define meaningful KPIs.)

• "Imagine you've found conflicting insights from quantitative data and qualitative feedback. How would you reconcile these and present a unified recommendation to the product team?" (Tests your ability to synthesize diverse data sources and handle ambiguity.)

• "How do you ensure your analytics recommendations are actionable and adopted by design and product teams?" (Focus on communication, stakeholder management, and influencing skills.) Company & Culture Questions:

• "What interests you about Capgemini, and specifically this UX Analytics Lead role?" (Research Capgemini's mission, recent projects, and how this role fits into their UX strategy.)

• "How do you approach mentoring junior team members or educating stakeholders on data insights?" (Demonstrate your leadership and communication style.)

• "Describe your experience working in a hybrid environment. How do you maintain collaboration and productivity?" (Align with Capgemini's work model.) Portfolio Presentation Strategy:

• Storytelling: Structure your portfolio presentation as a series of compelling stories, highlighting the challenge, your approach, and the impact.

• Visuals: Use clear, concise visuals (charts, graphs, dashboards) to illustrate your findings. Avoid overwhelming slides with text.

• Data Focus: Emphasize the data you used, the analytical techniques applied (Python, SQL, specific tools), and how you translated data into actionable insights.

• Impact Metrics: Clearly articulate the business impact of your work using quantifiable metrics.

• Q&A Readiness: Be prepared to answer in-depth questions about your methodology, assumptions, and the trade-offs you made during your projects.

📝 Enhancement Note: Expect a rigorous interview process that tests both technical proficiency and strategic thinking. Demonstrating how you drive measurable business outcomes through data analysis and collaboration will be key to success.

📌 Application Steps

To apply for this operations position:

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

• Tailor Your Resume: Highlight experience with Python, SQL, product analytics tools (Amplitude, Mixpanel, GA), A/B testing, and your track record in driving product decisions through data. Use keywords from the job description.

• Prepare Your Portfolio: Curate 2-3 strong case studies showcasing your UX analytics projects, emphasizing problem-solving, methodology, and measurable impact. Be ready to discuss them in detail.

• Practice Interview Questions: Rehearse answers to strategy, technical, and behavioral questions, focusing on the STAR method (Situation, Task, Action, Result) and incorporating specific examples from your experience.

• Research Capgemini: Understand their services, recent news, and commitment to UX and data. Familiarize yourself with their culture and values.

⚠️ 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.

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