Preparing for an interview at Genpact requires a balanced approach. You must demonstrate sharp technical skills while showing that you can communicate your ideas clearly and work collaboratively within a global team structure. Successful candidates are those who can explain not just how they built a solution, but why they chose that specific architecture over alternatives.

To stand out, you should focus your preparation on four key evaluation criteria that hiring managers prioritize:

Technical Rigor – You must demonstrate a deep, hands-on understanding of data engineering fundamentals. This means writing clean SQL queries, demonstrating proficiency in Python, and showcasing a strong grasp of distributed computing concepts using PySpark and Databricks.

Problem-Solving Ability – Interviewers want to see how you approach ambiguous data problems. You should be prepared to walk through your thought process, identify potential bottlenecks (such as data skew or resource constraints), and propose scalable, cost-effective solutions.

Adaptability & Continuous Learning – The technology landscape changes rapidly, and Genpact values engineers who can quickly master new tools. Be ready to highlight instances where you successfully learned a new framework or tool on the fly to meet a project need.

Client-Centric Communication – Because Genpact is a professional services organization, you must be able to translate complex technical architectures into clear business value. Your ability to articulate technical decisions to non-technical stakeholders or clients is highly valued.

The interview process for a Data Engineer at Genpact is designed to be thorough yet efficient. Candidates frequently report a fast-moving timeline, sometimes with multiple rounds completed within a single day or over a very short period. The process focuses heavily on evaluating your immediate technical capability alongside your fit for client-facing environments.

Depending on your experience level and how you apply (such as campus recruitment versus lateral hiring), the process generally consists of an initial online assessment, followed by intense technical discussions, and concludes with a managerial and HR evaluation. The technical rounds are highly practical, requiring you to write code and solve database problems in real time, while the managerial rounds focus on your past projects, architectural choices, and behavioral alignment.

The timeline shown above outlines the typical progression a candidate goes through from the initial application to the final offer stage. While the exact sequence can vary slightly based on the seniority of the role and the specific business unit, most candidates can expect to navigate these core stages. Use this timeline to pace your preparation, ensuring you allocate enough time to practice live coding before your technical rounds.

To help you focus your preparation, we have broken down the core evaluation areas of the Genpact technical interview. Expect your interviewers to drill deep into these specific domains.

SQL and Database Management (DBMS)

SQL is a foundational pillar of the Data Engineer role at Genpact. You must be highly proficient in writing, debugging, and optimizing SQL queries, as well as understanding database design principles.

Be ready to go over:

• Window Functions – Understanding how and when to use LEAD, LAG, ROW_NUMBER, and partitioning.
• Query Optimization – Analyzing execution plans, identifying bottlenecks, and utilizing indexing effectively.
• Schema Design – Designing star schemas, snowflake schemas, and understanding normalization versus denormalization.
• Advanced Concepts (less common) – Recursive CTEs, query partitioning strategies, and database lock management.

Example scenarios:

• "Given an employee table, write a query to find all employees who earn more than the average salary of their respective departments."
• "How would you design a database schema to track real-time inventory updates for a global e-commerce client?"

PySpark & Databricks Ecosystem

For modern data pipelines, Genpact relies heavily on PySpark and Databricks. You must understand how distributed computing works under the hood to build efficient, scalable pipelines.

Be ready to go over:

• Spark Architecture – Drivers, executors, slots, and how tasks are distributed across a cluster.
• Performance Tuning – Handling shuffle partitions, broadcast joins, and avoiding out-of-memory (OOM) errors.
• Delta Lake – Understanding ACID transactions, time travel, and upserts (MERGE operations) on delta tables.
• Advanced Concepts (less common) – Custom catalyst optimizer rules, spark-submit configurations, and integrating Spark with streaming sources.

Example scenarios:

• "Your PySpark job is taking an exceptionally long time during a join operation. Walk me through how you would diagnose and fix this issue."
• "Explain how you would implement an incremental data loading strategy using Databricks Auto Loader."

Python Coding & Algorithms

You will be asked to write code during your technical rounds. The focus is on writing clean, readable, and efficient Python code to solve algorithmic and data manipulation problems.

Be ready to go over:

• Data Structures – Effective use of lists, dictionaries, sets, and tuples.
• File Handling & Parsing – Reading, processing, and writing JSON, CSV, and parquet files using Python.
• Algorithms – Basic sorting, searching, and string manipulation algorithms.
• Advanced Concepts (less common) – Generators for memory efficiency, object-oriented programming in data pipelines, and concurrency.

Example scenarios:

• "Write a Python program that takes a list of log entries and outputs a count of occurrences for each unique error code."
• "Implement a basic algorithm to detect if a given string is a palindrome, optimizing for both time and space complexity."

Managerial & Scenario-Based Round

The managerial round goes beyond coding to assess your architectural thinking, project management skills, and professional demeanor.

Be ready to go over:

• Project Walkthroughs – Explaining the business context, technical choices, and outcomes of your past projects.
• Stakeholder Management – Handling shifting requirements, tight deadlines, and client communication.
• Adaptability – Demonstrating how you learn new technologies and guide team members through technical transitions.
• Advanced Concepts (less common) – Cost optimization strategies for cloud infrastructure and designing data governance frameworks.

Example scenarios:

• "Describe a time when a client changed their requirements halfway through a project. How did you manage the technical debt and deliver the project?"
• "If you are tasked with migrating an on-premises data warehouse to the cloud, what key factors would you consider in your migration strategy?"

As a Data Engineer at Genpact, your day-to-day work will bridge the gap between complex data infrastructure and strategic business outcomes. You will be responsible for designing and implementing the pipelines that ingest, transform, and store vast amounts of enterprise data. This involves writing scalable code, optimizing database performance, and ensuring that data is delivered accurately and securely to downstream analytics platforms.

Collaboration is a core component of this role. You will work closely with cross-functional teams, including product managers, data scientists, business analysts, and external clients. You will translate business requirements into technical specifications, ensuring that the data architectures you build are flexible enough to meet evolving analytical needs. Additionally, you will play a key role in data governance, ensuring that all pipelines comply with security standards and industry regulations.

Your typical projects will range from building real-time streaming pipelines for financial transaction monitoring to developing batch-processing workflows for supply chain optimization. You will also be tasked with continuously improving existing infrastructure, identifying performance bottlenecks, and migrating legacy systems to modern cloud environments like Azure, AWS, or GCP using Databricks and modern data warehousing solutions.