UI Dev

📍 Job Overview

Job Title: Data Engineer Company: Dentsu (Merkle Brand) Location: Bengaluru, India (Manyata N1 Block) Job Type: Full Time Category: Data Engineering / Operations Analytics Date Posted: 2025-09-23 Experience Level: Mid-Level (2-5 years) Remote Status: On-site

🚀 Role Summary

• Design, build, and maintain robust and scalable data pipeline architectures to support business operations and analytics.
• Ingest, clean, transform, and manipulate large, complex datasets, ensuring data integrity and adherence to business requirements.
• Drive operational efficiency and product innovation by identifying and implementing process improvements, including automation of manual tasks and optimization of data delivery.
• Develop and maintain analytics tools and infrastructure that provide actionable insights into key business metrics such as customer acquisition and operational efficiency.

📝 Enhancement Note: While the title is "UI Dev", the description clearly outlines responsibilities of a Data Engineer focused on data pipelines, data manipulation, and analytics infrastructure. This role is critical for enabling data-driven decision-making within Dentsu's operations. The "Merkle" brand affiliation suggests a focus on marketing and customer data.

📈 Primary Responsibilities

• Create and maintain optimal data pipeline architecture, ensuring reliability and scalability for diverse business needs.
• Assemble, clean, and transform large, complex datasets to meet functional and non-functional business requirements.
• Identify, design, and implement internal process improvements, including automating manual processes and optimizing data delivery methods.
• Build analytics tools that leverage data pipelines to deliver actionable insights on customer acquisition, operational efficiency, and other key business performance metrics.
• Collaborate effectively with stakeholders across Executive, Product, Data, and Design teams to address data-related technical issues and support their data infrastructure requirements.
• Ensure data separation and security within the operational and analytics databases.
• Develop data tools for analytics and data science teams to enhance their ability to build and optimize the company's product offerings.
• Work closely with data and analytics experts to improve the functionality and performance of the company's data systems.

📝 Enhancement Note: The responsibilities highlight a hands-on role in data infrastructure management, process optimization, and tool development, which are core functions within operations roles supporting analytics and business intelligence.

🎓 Skills & Qualifications

Education: A Bachelor's or Master's degree in Computer Science, Engineering, Statistics, or a related quantitative field is typically expected for a Data Engineer role of this nature.

Experience: 2-5 years of professional experience in data engineering, data management, or a closely related field, with a proven track record of building and maintaining data pipelines.

Required Skills:

• Proficiency in creating and maintaining optimal data pipeline architecture.
• Strong experience in assembling, cleaning, and manipulating large, complex datasets.
• Demonstrated ability to identify and implement internal process improvements, including automation and optimization of data delivery.
• Experience in building analytics tools for actionable insights and performance metrics.
• Solid understanding of database requirements, performance analysis, and troubleshooting.
• Excellent collaboration and communication skills to work with cross-functional teams and stakeholders.
• Knowledge of data security best practices and data separation techniques.

Preferred Skills:

• Experience with cloud data platforms (e.g., AWS, Azure, GCP).
• Familiarity with big data technologies (e.g., Spark, Hadoop).
• Knowledge of SQL and NoSQL databases.
• Experience with ETL/ELT tools and methodologies.
• Understanding of data warehousing concepts.
• Familiarity with programming languages commonly used in data engineering (e.g., Python, Java, Scala).

📝 Enhancement Note: The AI-identified key skills align well with a mid-level Data Engineer role. The "UI Dev" title discrepancy suggests that candidates with strong backend data skills, even if their title was different, should consider applying if they meet these technical requirements.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Demonstrate experience in designing and implementing scalable data pipeline architectures.
• Showcase projects involving the manipulation and transformation of complex datasets, highlighting data quality improvements.
• Provide examples of process improvements or automation initiatives that led to measurable efficiency gains in data delivery or processing.
• Include case studies of analytics tools or dashboards built to provide actionable business insights.

Process Documentation:

• Detailed documentation of data pipeline designs, including data flow, transformations, and error handling mechanisms.
• Examples of process optimization workflows, illustrating how manual tasks were automated or data delivery was improved.
• Evidence of performance monitoring and troubleshooting for data systems and pipelines.

📝 Enhancement Note: For operations roles, a portfolio is crucial. Candidates should prepare to walk through specific projects that highlight their data engineering capabilities, process improvement successes, and the impact of their work on business operations and analytics.

💵 Compensation & Benefits

Salary Range: For a Data Engineer with 2-5 years of experience in Bengaluru, India, the estimated annual salary range is ₹8,00,000 to ₹15,00,000. This estimate is based on industry benchmarks for similar roles and experience levels in major Indian tech hubs, considering the cost of living and market demand for data engineering talent.

Benefits:

• Comprehensive health insurance coverage for employees and dependents.
• Retirement savings plans or provident fund contributions.
• Paid time off, including vacation days, sick leave, and public holidays.
• Opportunities for professional development, training, and certifications.
• Potential for performance-based bonuses and incentives.
• Access to company-provided resources and tools for career growth.

Working Hours: The standard working hours are approximately 40 hours per week, typical for a full-time role. Flexibility may be offered, but core hours for team collaboration and meetings are expected.

📝 Enhancement Note: Salary figures are estimates for the Bengaluru region and may vary based on specific skills, negotiation, and the exact scope of responsibilities within Dentsu.

🎯 Team & Company Context

🏢 Company Culture

Industry: Advertising & Marketing Services / Digital Transformation. Dentsu is a global leader in advertising and marketing, with its Merkle brand specializing in customer experience management and data-driven marketing. This context implies a fast-paced environment focused on leveraging data to drive marketing outcomes and client success.

Company Size: Dentsu is a large, global organization. As of recent data, it employs tens of thousands of people worldwide. This size offers opportunities for diverse career paths and exposure to various projects and markets. Within this structure, the Merkle brand likely operates with a degree of autonomy, focusing on its specialized services.

Founded: Dentsu was founded in 1906. Merkle was founded in 1988 and acquired by Dentsu Aegis Network in 2016. This long history, combined with Merkle's focus on digital transformation, suggests a company that values both established practices and forward-thinking innovation.

Team Structure: The Data Engineer will likely be part of a broader Data, Analytics, or Technology team within Merkle India. This team may comprise data scientists, analysts, other engineers, and project managers. The structure would likely be matrixed, with engineers reporting to a manager while also working on project-specific teams that include stakeholders from client services, strategy, and product development.

Methodology: Expect a data-driven and agile approach to operations. This would involve iterative development cycles for data pipelines and tools, rigorous testing, and continuous feedback loops with stakeholders to ensure alignment with business objectives. Emphasis will be placed on data quality, efficiency, and the ability to derive actionable insights.

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

📝 Enhancement Note: Understanding Dentsu's position as a global marketing powerhouse and Merkle's specialization in CX and data is crucial for aligning one's skills and career aspirations with the company's operational needs.

📈 Career & Growth Analysis

Operations Career Level: This role is positioned as a Mid-Level Data Engineer, typically requiring 2-5 years of experience. It signifies a level where individuals are expected to work with moderate supervision, take ownership of specific tasks or components of larger projects, and contribute to the design and implementation of data solutions.

Reporting Structure: The Data Engineer will likely report to a Data Engineering Manager or a similar lead within the Merkle India technology or analytics division. They will work closely with data analysts, data scientists, and business stakeholders, requiring strong communication and collaboration skills to integrate their work effectively.

Operations Impact: This role directly impacts operational efficiency and business decision-making by ensuring the reliability and accessibility of data. By building robust data pipelines and analytics tools, the Data Engineer empowers teams to understand customer behavior, optimize marketing campaigns, and drive revenue growth. Their work is foundational for data-driven strategies.

Growth Opportunities:

• Technical Specialization: Opportunity to deepen expertise in specific data technologies, cloud platforms, or big data solutions, potentially leading to Senior Data Engineer or Data Architect roles.
• Cross-Functional Expertise: Develop a strong understanding of marketing analytics, customer experience management, and business operations by working closely with diverse teams.
• Leadership Potential: With proven performance, opportunities may arise to lead small teams, mentor junior engineers, or manage specific data projects.
• Industry Exposure: Gain valuable experience within the dynamic digital advertising and marketing industry, working with global clients and cutting-edge technologies.

📝 Enhancement Note: The career path for a Data Engineer in a company like Dentsu often involves both deepening technical skills and broadening business domain knowledge, particularly in marketing and customer analytics.

🌐 Work Environment

Office Type: The role is based on-site at Dentsu's Bengaluru office (Manyata N1 Block). This suggests a traditional office environment designed for collaboration, focused work, and team interaction.

Office Location(s): The primary location is Bengaluru, with an alternative option mentioned in Mumbai (Goregaon Prism Tower). This indicates that Dentsu has significant operations in major Indian cities.

Workspace Context:

• The office environment likely fosters collaboration, with open workspaces and meeting rooms designed for team discussions and project work.
• Access to modern technology infrastructure, including high-performance computing resources, development tools, and potentially cloud environments, will be available.
• Opportunities for regular interaction with data scientists, analysts, project managers, and business stakeholders, facilitating knowledge sharing and problem-solving.

Work Schedule: The standard full-time schedule is expected, likely aligning with typical business hours in India (e.g., 9 AM to 6 PM IST). Some flexibility may be available, but adherence to project deadlines and team availability for collaboration is key.

📝 Enhancement Note: Being on-site in a major tech hub like Bengaluru offers advantages in terms of networking, mentorship, and access to industry events, which are beneficial for career growth in operations and technology.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: A recruiter or HR representative will likely conduct an initial phone screen to assess basic qualifications, experience, and cultural fit. Be prepared to discuss your resume and interest in the role.
• Technical Interview(s): Expect one or more technical interviews focusing on data engineering concepts, SQL, programming (likely Python), data structures, algorithms, and system design for data pipelines.
• Portfolio Review/Case Study: A crucial step will involve presenting your portfolio or working through a live coding or system design case study related to data pipeline architecture, data manipulation, or building analytics tools.
• Hiring Manager/Team Interview: An interview with the hiring manager and potential team members to assess your problem-solving approach, collaboration style, and alignment with team dynamics and company culture.
• Final Round: Potentially a final interview with a senior leader to confirm fit and discuss overall career aspirations.

Portfolio Review Tips:

• Highlight Impact: For each project, clearly articulate the problem statement, your specific contributions, the technologies used, and most importantly, the business impact or results achieved (e.g., improved efficiency, faster data delivery, enabled new insights).
• Showcase Process: Demonstrate your understanding of the end-to-end data pipeline process, from data ingestion and cleaning to transformation and delivery for analysis.
• Technical Depth: Be ready to discuss the technical challenges you faced and how you overcame them, including design decisions for scalability, reliability, and performance.
• Code Samples: If possible, have well-documented code samples available (e.g., on GitHub) that showcase your programming and data manipulation skills.
• Conciseness: Prepare a concise and compelling narrative for each portfolio item, focusing on the most relevant aspects for the Data Engineer role.

Challenge Preparation:

• SQL Proficiency: Practice complex SQL queries, including joins, subqueries, window functions, and performance optimization.
• Python for Data: Brush up on Python libraries relevant to data engineering, such as Pandas, SQLAlchemy, and potentially libraries for interacting with cloud services or big data frameworks.
• Data Modeling & Architecture: Understand different data modeling techniques (e.g., star schema, snowflake schema) and principles of designing scalable data architectures.
• ETL/ELT Concepts: Be prepared to discuss ETL/ELT processes, tools, and best practices.
• Problem-Solving: Practice breaking down complex data problems into smaller, manageable steps and articulating your thought process clearly.

📝 Enhancement Note: Given the company's focus on marketing and client data, expect interview questions to probe your ability to translate business requirements into technical data solutions and to understand the impact of data quality on marketing outcomes.

🛠 Tools & Technology Stack

Primary Tools:

• SQL: Essential for data querying, manipulation, and database management.
• Python: Widely used for scripting, data manipulation (Pandas), automation, and building data pipelines.
• Cloud Platforms: Experience with AWS (e.g., S3, Redshift, Glue, EMR), Azure (e.g., Blob Storage, Synapse Analytics, Data Factory), or GCP (e.g., Cloud Storage, BigQuery, Dataflow) is highly beneficial.
• ETL/ELT Tools: Familiarity with tools like Apache Airflow, Talend, Informatica, or cloud-native services (e.g., AWS Glue, Azure Data Factory).

Analytics & Reporting:

• Data Warehousing: Experience with solutions like Snowflake, Amazon Redshift, Google BigQuery, or Azure Synapse Analytics.
• BI Tools: Knowledge of tools such as Tableau, Power BI, Looker, or QlikView for data visualization and reporting is a plus.

CRM & Automation:

• CRM Systems: Understanding of how data flows from and interacts with CRM systems like Salesforce or Adobe Experience Cloud.
• Data Integration Platforms: Familiarity with tools or methods for integrating various data sources.

📝 Enhancement Note: The specific stack will vary, but a strong foundation in SQL, Python, and at least one major cloud data platform is expected for a role supporting a global marketing organization.

👥 Team Culture & Values

Operations Values:

• Data-Driven: A strong emphasis on using data to inform decisions, measure performance, and drive strategy. Operations professionals are expected to be analytical and evidence-based.
• Client-Centric: Dentsu and Merkle are client-facing organizations. This means understanding client needs and delivering solutions that provide tangible value and drive business outcomes for them.
• Innovation & Agility: A culture that encourages exploring new technologies and methodologies to stay ahead in the rapidly evolving digital marketing landscape. Adaptability and a willingness to learn are key.
• Collaboration: Teams work together across functions (data, technology, client services, strategy) to achieve common goals. Open communication and teamwork are vital.
• Efficiency & Optimization: A continuous drive to improve processes, automate tasks, and optimize data systems for better performance and cost-effectiveness.

Collaboration Style:

• Expect a highly collaborative environment where cross-functional teams work together on projects.
• Regular communication through meetings, project management tools, and instant messaging platforms is common.
• A culture of constructive feedback and knowledge sharing is likely encouraged to foster continuous improvement.

📝 Enhancement Note: For operations roles within marketing services, the ability to bridge technical data capabilities with business and client objectives is a highly valued trait.

⚡ Challenges & Growth Opportunities

Challenges:

• Data Complexity & Volume: Handling vast and diverse datasets from various sources requires robust engineering practices and efficient processing.
• Rapid Technological Change: The digital marketing and data technology landscape evolves quickly, requiring continuous learning and adaptation to new tools and techniques.
• Translating Business Needs: Effectively translating complex business and marketing requirements into functional data solutions can be challenging.
• Data Quality & Governance: Ensuring data accuracy, consistency, and compliance with privacy regulations (e.g., GDPR, CCPA) is a critical and ongoing challenge.

Learning & Development Opportunities:

• Advanced Data Technologies: Opportunities to gain expertise in big data technologies, cloud data services, and modern data warehousing solutions.
• Marketing Analytics Domain: Deepen understanding of marketing analytics, customer journey mapping, campaign performance measurement, and customer data platforms.
• Certifications: Pursue certifications from cloud providers (AWS, Azure, GCP) or specialized data engineering training.
• Mentorship: Benefit from working alongside experienced data professionals and potentially gain mentorship from senior engineers or architects.

📝 Enhancement Note: Tackling these challenges head-on provides significant opportunities for professional growth and skill development, making this a rewarding role for ambitious data engineers.

💡 Interview Preparation

Strategy Questions:

• "Describe a complex data pipeline you designed and built. What were the key challenges, and how did you ensure scalability and reliability?" (Focus on architecture, problem-solving, and impact.)
• "How do you approach data quality issues? Walk me through a process you implemented to improve data accuracy or consistency." (Highlight your methodology for data governance and validation.)
• "Imagine you need to build an analytics tool to track customer acquisition cost for a new marketing campaign. What data sources would you use, what transformations would be needed, and what would the pipeline look like?" (Demonstrate your ability to translate business needs into technical designs.)
• "How do you stay updated with the latest trends and technologies in data engineering and big data?" (Showcase your commitment to continuous learning.)

Company & Culture Questions:

• "Based on your understanding of Dentsu and Merkle, how do you see this role contributing to our data-driven marketing strategies?" (Research the company's mission and Merkle's offerings.)
• "Describe a time you had to collaborate with non-technical stakeholders. How did you ensure clear communication and alignment on data-related projects?" (Focus on communication and stakeholder management skills.)
• "What are your expectations regarding teamwork and collaboration within a data engineering team?" (Align your style with the company's collaborative culture.)

Portfolio Presentation Strategy:

• Problem-Solution-Result: Structure your presentation around the problem you solved, the solution you engineered, and the measurable results or business impact.
• Visual Aids: Use diagrams, flowcharts, and screenshots to illustrate your data pipeline designs and analytics tools effectively.
• Technical Deep Dive: Be prepared to answer detailed questions about your code, design choices, and the trade-offs you made.
• Concise Narrative: Practice delivering your portfolio overview efficiently, highlighting the most relevant achievements for the role.

📝 Enhancement Note: Tailor your responses to Dentsu's specific industry (advertising/marketing) and the potential focus on customer data and campaign performance.

📌 Application Steps

To apply for this Data Engineer position:

• Submit your application through the provided Workday link.
• Tailor your Resume: Ensure your resume highlights experience with data pipelines, data manipulation, SQL, Python, and any relevant cloud or big data technologies. Quantify achievements where possible (e.g., "improved data processing speed by 30%").
• Prepare Your Portfolio: Gather 2-3 key projects that best showcase your data engineering skills, process improvement initiatives, and analytical tool development. Be ready to present them concisely.
• Research Dentsu & Merkle: Understand the company's services, values, and recent work, especially concerning data and marketing analytics.
• Practice Technical Concepts: Review core data engineering principles, SQL querying, and Python for data manipulation. Be ready for live coding or system design challenges.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and operations industry-standard assumptions based on the provided details and the "Data Engineer" role context. While the original job title was "UI Dev," the description strongly indicates a Data Engineering function. Candidates should carefully review the responsibilities to ensure alignment with their skills and career goals. All details should be verified directly with the hiring organization before making application decisions.