Associate Technical Architect - UI

📍 Job Overview

Job Title: Associate Technical Architect - UI

Company: Dentsu (Merkle Brand)

Location: Mumbai, India (DGS India - Thane Ashar IT Park)

Job Type: Full-Time

Category: Technical Architecture / Data Science & Analytics

Date Posted: May 12, 2026

Experience Level: 5-10 Years (Mid-Senior Level)

Remote Status: On-site

🚀 Role Summary

• This role bridges the gap between technical architecture and data science, focusing on front-end (UI) aspects within a data-driven marketing context.

• It involves leading customer analysis deep dives and translating strategic insights into actionable technical solutions for clients.

• The position requires strong project management capabilities, including the ability to independently manage data science projects from inception to delivery.

• A significant component is team mentorship, guiding analysts in their technical and career development within data science and analytics.

📝 Enhancement Note: While the title "Associate Technical Architect - UI" suggests a primary focus on UI architecture, the core responsibilities and keywords heavily lean into Data Science and Analytics leadership. This augmentation assumes the UI aspect is crucial for how data insights are presented and interacted with by clients, or that the role involves architecting data visualization platforms and client-facing analytics interfaces. The role requires a blend of technical architecture, data science application, and client management.

📈 Primary Responsibilities

• Lead and manage deep-dive customer analysis initiatives, identifying key trends and insights to inform client strategies.

• Execute data science strategies outlined by senior leadership, ensuring alignment with client objectives and business goals.

• Oversee and manage day-to-day communication and relationship management with clients, serving as a key point of contact for technical and analytical matters.

• Integrate disparate datasets from various sources, ensuring data quality and consistency for robust analysis.

• Perform data preparation and transformation tasks to make data suitable for advanced analytics and machine learning models.

• Apply advanced data science methodologies and techniques to generate actionable insights and strategic recommendations for clients.

• Maintain up-to-date knowledge of emerging trends, tools, and best practices in the data science and technical architecture industries.

• Independently manage the end-to-end lifecycle of data science projects, ensuring timely and high-quality delivery to clients.

• Develop, maintain, and optimize code used for analytics, data manipulation, and model deployment.

• Clearly and effectively communicate complex findings, insights, and technical recommendations to both technical and non-technical stakeholders, including clients.

• Mentor and guide a team of analysts, fostering their technical skills, analytical capabilities, and career progression.

📝 Enhancement Note: The responsibilities indicate a hybrid role requiring strong analytical and data science skills, coupled with technical architecture and client-facing capabilities. The emphasis on "UI" in the title suggests the role may involve architecting client-facing dashboards, data visualization tools, or front-end applications that present data insights, rather than traditional front-end development.

🎓 Skills & Qualifications

Education:

Experience:

• 5-10 years of progressive experience in technical architecture, data science, advanced analytics, or a related field.

• Proven experience in managing and delivering data science projects from concept to completion.

• Demonstrated experience in client-facing roles, including communication, relationship building, and project management.

Required Skills:

• Data Science & Analytics: Strong proficiency in applying data science methods, statistical modeling, machine learning algorithms, and data analysis techniques.

• Data Integration & Preparation: Expertise in integrating data from various sources, data cleaning, transformation, and preparing datasets for analysis.

• Technical Architecture: Understanding of architectural principles for data platforms, analytics solutions, and client-facing applications, with a focus on UI integration.

• Client Management & Communication: Excellent verbal and written communication skills, with the ability to articulate complex technical and analytical concepts to diverse audiences.

• Project Management: Proven ability to manage multiple projects simultaneously, prioritize tasks, and ensure timely delivery within scope and budget.

• Programming & Scripting: Proficiency in languages commonly used in data science and analytics, such as Python (with libraries like Pandas, NumPy, Scikit-learn) and/or R.

• SQL: Strong command of SQL for data extraction, manipulation, and querying relational databases.

Preferred Skills:

• UI/UX Design Principles: Familiarity with UI/UX design principles to effectively translate data insights into user-friendly and impactful interfaces.

• Data Visualization Tools: Experience with tools like Tableau, Power BI, Looker, or custom JavaScript libraries (e.g., D3.js) for creating interactive dashboards and visualizations.

• Cloud Platforms: Experience with cloud data services (AWS, Azure, GCP) for data storage, processing, and deployment.

• Big Data Technologies: Familiarity with big data ecosystems like Hadoop, Spark, or distributed computing frameworks.

• Agile Methodologies: Experience working in Agile development environments.

📝 Enhancement Note: The "UI" in the title is interpreted as encompassing the presentation layer of data insights. The preferred skills reflect this by including data visualization and UI/UX principles, suggesting the role might involve architecting how data is consumed by clients through interfaces, rather than pure front-end code development.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Data Science Project Case Studies: Detailed examples of past data science projects, showcasing the problem statement, methodology, data used, insights generated, and measurable impact.

• Technical Architecture Designs: Visualizations or documentation of system architectures, particularly those related to data pipelines, analytics platforms, or client-facing applications where data is presented.

• Data Integration & ETL Workflows: Documentation or examples of processes used to integrate disparate data sources and prepare data for analysis.

• Client Communication Samples: Examples (anonymized) of reports, presentations, or dashboards used to communicate findings and recommendations to clients, demonstrating clarity and impact.

• Code Repository Access (Optional but Recommended): Links to GitHub or similar repositories showcasing well-documented, efficient code for analytics, data manipulation, or model implementation.

Process Documentation:

• Workflow Design and Optimization: Evidence of designing and optimizing end-to-end data science workflows, from data ingestion to insight delivery.

• Implementation and Automation: Examples of automating repetitive tasks, deploying models, or implementing analytics solutions within a production environment.

• Measurement and Performance Analysis: Documentation of how the performance of data science models and analytics solutions was measured and iteratively improved, including A/B testing or other validation methods.

📝 Enhancement Note: For an Associate Technical Architect role with a UI/Data Science blend, the portfolio should demonstrate not only analytical rigor but also the ability to translate those insights into tangible, user-facing solutions. Emphasis should be on the end-to-end process and the impact of the implemented solutions.

💵 Compensation & Benefits

Salary Range:

• Based on industry benchmarks for Associate Technical Architects and Data Science leads with 5-10 years of experience in Mumbai, India, the estimated annual salary range is ₹15,00,000 to ₹25,00,000. This estimate accounts for the blend of technical architecture, data science expertise, client management, and team leadership responsibilities.

Benefits:

• Comprehensive health insurance (medical, dental, vision) for employees and dependents.

• Provident Fund (PF) and Gratuity contributions as per Indian labor laws.

• Paid time off, including vacation days, sick leave, and national holidays.

• Opportunities for professional development, including training, certifications, and conference attendance.

• Performance-based bonuses and incentives.

• Employee assistance programs (EAP) for mental and emotional well-being.

• Access to Dentsu's global network and resources.

Working Hours:

• Standard full-time work hours are typically 40 hours per week, Monday to Friday.

• The role may require flexibility to accommodate client needs across different time zones or critical project deadlines.

📝 Enhancement Note: Salary ranges for technical roles in major Indian cities like Mumbai are competitive. The estimate is based on current market data for similar positions, considering the hybrid nature of the role which combines technical architecture, advanced analytics, and client-facing responsibilities.

🎯 Team & Company Context

🏢 Company Culture

Industry: Marketing and Advertising Technology, Data Analytics, Consulting. Dentsu is a global leader in marketing and advertising, with Merkle being a key agency specializing in customer experience management and data-driven marketing.

Company Size: Dentsu Aegis Network (DAN) is a very large global organization, employing over 60,000 people worldwide. Merkle itself is a significant entity within this network. This size offers stability, extensive resources, and a broad range of project opportunities.

Founded: Dentsu was founded in 1906, and Merkle was founded in 1988. The long history indicates a stable and established industry presence.

Team Structure:

• The role is within the Merkle brand, likely operating within a data science, analytics, or technical consulting practice.

• The team will likely consist of data scientists, analysts, data engineers, and other technical specialists.

• The Associate Technical Architect will report to a senior leader (e.g., Director of Data Science, VP of Technology) and will manage a team of analysts.

Methodology:

• Data-Driven Decision Making: Emphasis on using data to inform strategies, client recommendations, and internal process improvements.

• Agile Project Management: Likely adoption of Agile methodologies for project delivery, allowing for flexibility and iterative development.

• Client-Centric Approach: Focus on understanding client business challenges and delivering tailored, impactful solutions.

• Continuous Learning & Innovation: Encouragement to stay abreast of new technologies and methodologies in data science and marketing tech.

Company Website: https://www.dentsu.com/, https://www.merkle.com/

📝 Enhancement Note: Dentsu and Merkle operate in a fast-paced, results-oriented environment. The culture likely values innovation, collaboration, and a commitment to delivering measurable value for clients through data and technology.

📈 Career & Growth Analysis

Operations Career Level: This role is positioned at a mid-to-senior level, often referred to as an "Associate" or "Lead" architect. It requires a solid foundation of technical and analytical skills, combined with the ability to lead projects and mentor teams, but still with room for growth into more senior architectural or management roles.

Reporting Structure: The Associate Technical Architect will report to a senior leader within the data science or technology department and will have direct reports (analysts). This structure provides exposure to both strategic leadership and team management.

Operations Impact: This role has a direct impact on client success by translating complex data into actionable insights and architecting the technical solutions that deliver these insights. Success in this role contributes directly to client retention, revenue growth for Merkle/Dentsu, and the company's reputation as a data-driven marketing leader.

Growth Opportunities:

• Technical Specialization: Deepen expertise in specific areas of data science, machine learning, cloud architecture, or UI development for data visualization.

• Leadership Progression: Advance to Senior Technical Architect, Lead Data Scientist, or a management role overseeing larger teams or practice areas.

• Client Relationship Management: Develop stronger client engagement skills, potentially leading to strategic advisory roles.

• Industry Certifications: Pursue advanced certifications in cloud platforms, data science, or project management.

• Cross-functional Mobility: Opportunities to move into related areas such as product management for analytics platforms or strategic consulting.

📝 Enhancement Note: The "Associate" title indicates a stepping stone role. Candidates should look for opportunities to demonstrate leadership potential and strategic thinking to pave the way for more senior positions within Dentsu's global structure.

🌐 Work Environment

Office Type: The role is based in a modern IT Park office environment (Ashar IT Park, Mumbai), which typically offers professional workspaces, meeting rooms, and collaborative areas.

Office Location(s): DGS India - Mumbai - Thane Ashar IT Park. This location is a well-established business hub in Mumbai, providing good infrastructure and accessibility. The mention of "Bangalore, India" as an alternative location suggests potential for hybrid work or opportunities within other Dentsu offices.

Workspace Context:

• Collaborative Environment: Expect a dynamic workspace designed to foster teamwork, with open areas for collaboration and dedicated meeting rooms for client interactions and team discussions.

• Technology Access: Access to high-performance computing resources, relevant software licenses, and potentially dedicated development/testing environments.

• Team Interaction: Regular opportunities to interact with a diverse team of professionals, including data scientists, analysts, project managers, and client stakeholders, facilitating knowledge sharing and problem-solving.

Work Schedule: While a standard 40-hour work week is expected, the nature of client-facing roles and project deadlines may necessitate occasional flexibility. The company culture will likely support work-life balance, but proactive communication regarding availability is key.

📝 Enhancement Note: Working in an IT park in Mumbai suggests a professional, corporate setting. The environment is likely geared towards productivity and collaboration, with all necessary amenities and technological support.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: HR or Recruiter screen to assess basic qualifications, experience, and cultural fit.

• Technical Interview(s): In-depth discussions covering data science concepts, programming skills (Python/R, SQL), data architecture principles, and UI/UX considerations for data presentation. Expect coding challenges or live coding exercises.

• Case Study Presentation: Candidates may be asked to present a past project from their portfolio, focusing on problem-solving, methodology, insights, and impact. This is a crucial step for this role.

• Team/Manager Interview: Assessment of leadership potential, team mentorship approach, client management skills, and overall fit with the team's working style.

• Final Interview: Potentially with a senior leader to discuss strategic thinking, career aspirations, and final offer details.

Portfolio Review Tips:

• Highlight End-to-End Projects: Showcase projects that demonstrate the full lifecycle from data ingestion and analysis to the architecting and presentation of insights (UI).

• Quantify Impact: For each project, clearly articulate the business problem, the solution implemented, and the measurable business outcomes (e.g., revenue increase, cost savings, improved conversion rates). Use specific metrics.

• Showcase Technical Depth and Breadth: Include examples that highlight your proficiency in data science techniques, coding, data architecture, and any relevant UI/visualization skills.

• Structure for Clarity: Organize your portfolio logically. For case studies, use a clear narrative: Problem -> Approach -> Solution -> Results -> Learnings.

• Prepare to Discuss Your Role: Be ready to discuss your specific contributions, challenges faced, and how you overcame them, especially in team-based projects.

Challenge Preparation:

• Technical Challenges: Practice coding problems in Python/R and SQL, focusing on data manipulation, algorithm implementation, and query optimization.

• System Design/Architecture: Be prepared to discuss architectural patterns for data platforms, analytics solutions, and how to design user interfaces for data visualization.

• Data Science Scenarios: Think through how you would approach common data science problems (e.g., customer segmentation, churn prediction, campaign optimization) given specific business contexts.

• Client Communication Scenarios: Practice explaining complex technical concepts or analytical findings concisely and persuasively to a non-technical audience.

📝 Enhancement Note: The portfolio is critical for this role. Candidates should prepare detailed case studies that not only demonstrate technical prowess but also their ability to translate technical solutions into client-facing, impactful results.

🛠 Tools & Technology Stack

Primary Tools:

• Programming Languages: Python (with libraries like Pandas, NumPy, Scikit-learn, TensorFlow/PyTorch), R.

• Databases & Querying: SQL, PostgreSQL, MySQL, NoSQL databases.

• Cloud Platforms: Experience with AWS, Azure, or GCP services for data storage (S3, ADLS, GCS), processing (EMR, Databricks, HDInsight), and machine learning (SageMaker, Azure ML, Vertex AI).

Analytics & Reporting:

• Data Visualization: Tableau, Power BI, Looker, QlikView, or custom JavaScript libraries (D3.js, Chart.js) for building interactive dashboards and reports.

• BI Tools: Expertise in leveraging Business Intelligence platforms for data exploration and reporting.

CRM & Automation:

• CRM Systems: Familiarity with CRM platforms like Salesforce, Microsoft Dynamics, HubSpot for understanding customer data context.

• ETL/Data Integration Tools: Experience with tools like Informatica, Talend, Apache NiFi, or custom scripting for data pipelines.

• Version Control: Git, GitHub, GitLab, Bitbucket for code management and collaboration.

📝 Enhancement Note: The technology stack is broad, reflecting the hybrid nature of the role. Proficiency in Python and SQL is fundamental. Experience with cloud platforms and data visualization tools is highly advantageous, especially for the UI aspect of the role.

👥 Team Culture & Values

Operations Values:

• Data-Driven Excellence: A commitment to rigorous data analysis, statistical accuracy, and deriving meaningful insights that drive business value.

• Client Focus: Prioritizing client needs and delivering solutions that exceed expectations, fostering long-term partnerships.

• Innovation & Curiosity: Encouraging exploration of new technologies, methodologies, and creative problem-solving approaches.

• Collaboration & Teamwork: Valuing diverse perspectives and fostering an environment where team members support each other to achieve common goals.

• Integrity & Accountability: Upholding ethical standards in data handling and analysis, and taking ownership of project outcomes.

Collaboration Style:

• Cross-Functional Integration: Actively engaging with account managers, strategy teams, creative departments, and other technical specialists to ensure holistic client solutions.

• Open Communication: Fostering an environment where ideas can be freely shared, constructive feedback is welcomed, and challenges are addressed transparently.

• Knowledge Sharing: Encouraging the dissemination of best practices, new learnings, and technical expertise across the team and wider organization.

• Agile & Iterative: Embracing iterative development and feedback loops to continuously improve solutions and processes.

📝 Enhancement Note: Dentsu and Merkle are known for their collaborative and client-centric cultures. The emphasis will be on teamwork, innovation, and delivering measurable results through data and technology.

⚡ Challenges & Growth Opportunities

Challenges:

• Data Complexity & Silos: Integrating and working with diverse, often fragmented, data sources across various client systems.

• Translating Technical to Business Value: Effectively communicating complex technical and analytical outcomes in terms of tangible business benefits to clients.

• Rapid Technological Evolution: Keeping pace with the continuously evolving landscape of data science tools, techniques, and architectural patterns.

• Managing Client Expectations: Balancing client demands with technical feasibility, project scope, and available resources.

• Team Development: Effectively mentoring and guiding analysts with varying levels of experience and skill sets.

Learning & Development Opportunities:

• Advanced Data Science & ML: Opportunities to work on cutting-edge machine learning projects and gain expertise in specialized areas.

• Technical Architecture Mastery: Developing deeper skills in designing scalable, robust, and secure data architectures.

• Client Advisory Skills: Enhancing capabilities in strategic consulting and providing high-level counsel to clients.

• Leadership Development Programs: Access to Dentsu's internal leadership training and development programs.

• Industry Conferences & Workshops: Support for attending relevant conferences and workshops to stay ahead of industry trends.

📝 Enhancement Note: This role offers significant opportunities for growth by tackling complex data challenges and mastering both technical architecture and client-facing communication skills. The continuous evolution of the data and marketing tech landscape presents ongoing learning opportunities.

💡 Interview Preparation

Strategy Questions:

• "Describe a complex data science project you led from inception to delivery. What was the business problem, your approach, the key insights, and the measurable impact?" (Focus on structured problem-solving and outcome quantification).

• "How would you architect a client-facing dashboard that visualizes campaign performance data, ensuring it's both informative and user-friendly?" (Assess architectural thinking and UI/UX awareness).

Company & Culture Questions:

• "What interests you about Dentsu/Merkle and this specific role as an Associate Technical Architect - UI?" (Showcase research into the company's work, values, and how your skills align).

• "How do you approach mentoring junior team members to foster their growth in data science and analytics?" (Assess leadership and mentorship style).

Portfolio Presentation Strategy:

• Curate Select Projects: Choose 2-3 diverse projects that best demonstrate your skills in data science, technical architecture, UI/data visualization, and client communication.

• Quantify Everything: For each project, clearly state the business problem, your specific role and contributions, the methodologies used, the key insights derived, and the quantifiable business impact.

• Visuals are Key: Use clear, concise slides with strong visuals (diagrams, charts, mockups) to illustrate your work. For UI-related projects, show the actual interface or mockups.

• Tell a Story: Structure your presentation as a narrative – the challenge, your journey to solve it, and the successful outcome.

• Be Prepared for Deep Dives: Anticipate detailed questions about your technical choices, problem-solving approach, and the rationale behind your decisions.

📝 Enhancement Note: Interviewers will be looking for a candidate who can not only perform complex technical tasks but also lead, mentor, and effectively communicate value to clients. The portfolio presentation is a critical opportunity to showcase this holistic capability.

📌 Application Steps

To apply for this operations position:

• Submit your application through the provided Workday link.

• Customize Your Resume: Tailor your resume to highlight keywords and responsibilities mentioned in this description, such as "Data Science," "Technical Architecture," "UI," "Client Management," "Data Integration," "Project Management," and "Team Mentorship." Quantify your achievements with specific metrics wherever possible.

• Prepare Your Portfolio: Select your strongest case studies that demonstrate your end-to-end project experience, including data science, technical architecture, UI/data visualization, and client communication. Ensure you can clearly articulate the business impact of your work.

• Research Dentsu & Merkle: Understand their business, recent campaigns, and their approach to data-driven marketing. Familiarize yourself with Merkle's offerings and Dentsu's global presence.

• Practice Interview Questions: Rehearse answers to common technical, behavioral, and situational interview questions, particularly those related to data science project leadership, technical architecture design, and client interaction. Prepare your portfolio presentation.

⚠️ 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.