A Data Analyst at Genpact plays a pivotal role in driving digital transformation, operational excellence, and data-driven decision-making for some of the world’s largest enterprises. Operating at the intersection of technology, business strategy, and industry-specific domain expertise, analysts here do not merely generate reports; they design solutions that directly impact business outcomes. Whether optimizing supply chains, enhancing financial operations, or streamlining aviation logistics, analysts transform raw data into highly strategic assets.

At Genpact, the scale and complexity of the data you will manage are immense. You will work alongside cross-functional teams of data scientists, engineers, and industry consultants to build robust data pipelines, design interactive business intelligence dashboards, and conduct deep-dive exploratory analysis. The insights you generate will be utilized by executive leadership and key client stakeholders to mitigate risks, identify new revenue streams, and improve operational efficiency globally.

What makes this role uniquely compelling is the sheer variety of challenge and domain exposure. For instance, in specialized teams like the aviation division, a Data Analyst might focus on mapping complex fleet operations, predicting maintenance needs, and analyzing critical safety metrics. To succeed, you must possess strong technical acumen, a structured approach to problem-solving, and the ability to translate technical findings into clear, business-oriented recommendations.

To help you prepare effectively, we have analyzed real interview experiences to identify the core themes and questions you are highly likely to encounter. While the exact questions may vary depending on the specific team and location, they consistently target your technical proficiency, structured coding abilities, and behavioral alignment with Genpact's collaborative culture.

SQL & Database Querying

This category tests your ability to retrieve, manipulate, and analyze structured data efficiently. Expect a strong focus on advanced querying techniques rather than basic selections.

• Write a SQL query using window functions (such as ROW_NUMBER(), RANK(), or DENSE_RANK()) to identify the top-performing business units.
• What is a derived query, and how does it differ from a Common Table Expression (CTE) in terms of readability and performance?

Preparing for an interview at Genpact requires a balanced approach that demonstrates both deep technical competence and sharp business acumen. You should not only focus on memorizing syntax or algorithms but also on how you apply these tools to solve actual business problems. Your interviewers want to see how you think, structure your thoughts, and communicate complex technical ideas to non-technical stakeholders.

Technical Competence – This is evaluated through live coding exercises, SQL query writing, and tool-specific deep dives. You must demonstrate a strong command of relational databases, data visualization principles, and data manipulation languages.

Structured Problem-Solving – Interviewers look at how you deconstruct ambiguous business problems. Strong candidates outline their assumptions, break down the problem into logical components, and propose structured, scalable analytical frameworks.

Communication & Stakeholder Management – As a consultant and analyst, you must present data clearly. You will be evaluated on your ability to explain complex technical designs, justify your choice of metrics, and tell a compelling story with data.

Cultural Fit & Adaptability – Genpact operates in a fast-paced, client-centric environment. Interviewers assess your resilience, your willingness to learn new domains quickly, and your ability to collaborate effectively across global teams.

The interview process for a Data Analyst at Genpact is comprehensive, structured, and designed to thoroughly evaluate your technical capability and cultural alignment. Depending on the location and specific business unit—such as aviation, financial services, or digital operations—the process typically spans several weeks and includes both virtual and in-person assessments. The company emphasizes a thorough, respectful evaluation process where candidates are given ample opportunity to showcase their practical skills.

The journey generally begins with an initial HR screening, followed by two distinct technical rounds. These technical rounds focus heavily on live coding, system queries, and business intelligence tool architecture. If you pass the technical evaluations, you will move to a final managerial and behavioral round where the focus shifts to project experience, domain knowledge, and cultural fit.

The timeline above illustrates the standard progression from your initial contact to the final decision. You should expect the technical evaluations to be the most rigorous phases of this journey, requiring focused preparation on core coding and database concepts. Use this timeline to pace your study plan, ensuring you are fully prepared for the deep-dive technical rounds before moving into the final behavioral discussions.

To excel in the Genpact interview process, you must understand the specific competencies your interviewers are trained to evaluate. This section breaks down the core technical and analytical areas you will face during your technical rounds.

SQL and Relational Databases

SQL is the cornerstone of the Data Analyst role at Genpact. You will be tested on your ability to write clean, efficient, and syntactically correct queries under pressure. Interviewers want to see that you can write complex queries to extract specific business insights from highly normalized databases.

Be ready to go over:

• Window Functions – Deep understanding of ROW_NUMBER(), RANK(), DENSE_RANK(), LEAD(), and LAG() for running calculations across sets of rows.
• Derived Queries & CTEs – Knowing when to use subqueries versus Common Table Expressions to structure complex, multi-step data transformations.
• Joins and Aggregations – Handling complex multi-table joins, understanding the performance implications of outer joins, and masterfully grouping data.
• Advanced concepts (less common) – Query optimization techniques, indexing strategies, and understanding execution plans to diagnose slow-running database queries.

Example questions or scenarios:

• "Write a query to find the second-highest transaction amount for each customer segment using a window function."
• "Explain a scenario where a derived query would perform better than a standard subquery, and write the syntax for both."

Business Intelligence and Power BI

For visualization-focused roles, Power BI proficiency is evaluated in depth. The focus is less on basic drag-and-drop mechanics and more on your understanding of data modeling, DAX performance, and dashboard optimization.

Be ready to go over:

• DAX Filter Context – Mastering how functions like CALCULATE, FILTER, and ALL manipulate the active filter context in a report.
• Performance Optimization – Techniques to reduce dashboard load times, such as reducing visual complexity, optimizing data models, and limiting calculated columns in favor of measures.
• Time Intelligence Functions – Writing robust DAX formulas for year-over-year, month-to-date, and rolling average calculations.
• Advanced concepts (less common) – Row-Level Security (RLS) implementation, incremental refresh configurations, and direct query optimization versus import mode.

Example questions or scenarios:

• "How do you resolve a circular dependency error in a complex Power BI data model containing multiple many-to-many relationships?"
• "Compare the behavior of RELATED and LOOKUPVALUE in DAX, explaining the performance overhead associated with each."

Data Structures, Algorithms & Problem Solving

Depending on the team's technical requirements, you will face a live coding round. This round evaluates your foundational programming skills, analytical logic, and familiarity with core computer science concepts.

Be ready to go over:

• Basic Array and String Manipulation – Writing clean code in Python or R to clean, parse, and restructure raw text or numeric data.
• Search and Sort Fundamentals – Demonstrating a conceptual understanding of basic search algorithms and their computational efficiency.
• Logic and Flow Control – Using loops, conditional statements, and error-handling blocks to build resilient, reusable data processing scripts.
• Advanced concepts (less common) – Basic complexity analysis (Big O notation) and implementing simple custom data structures for optimized data retrieval.

Example questions or scenarios:

• "Write a Python function that takes a list of integers and returns the duplicate elements without using built-in set operations."
• "Walk through the logical steps and pseudo-code required to merge two unsorted arrays of data while maintaining a sorted output."