Tech Mahindra Data Engineer Interview Questions + Guide in 2025

Tech Mahindra is a leading provider of digital transformation, consulting, and business re-engineering services, dedicated to delivering innovative solutions that enhance customer experiences across various industries.

As a Data Engineer at Tech Mahindra, you will be responsible for designing, developing, and maintaining scalable data pipelines to support data processing and analysis. You will work with various ETL processes and tools, particularly Azure Data Factory (ADF), and programming languages such as SQL and Python. Your role will involve collaborating closely with business stakeholders to understand their data needs and translating these requirements into technical solutions. You will also be expected to have a strong grasp of data modeling, cloud technologies, and data visualization techniques using tools like Power BI. Key responsibilities include ensuring data quality, optimizing data workflows, and implementing best practices for data governance.

A successful candidate for this role should possess strong analytical and problem-solving skills, be proficient in data processing techniques, and demonstrate excellent communication abilities to convey complex technical concepts to non-technical stakeholders. A background in Agile methodologies and experience with databases like SAP will also be beneficial.

This guide will help you prepare effectively for your interview by equipping you with insights into the role's expectations, essential skills, and the company culture at Tech Mahindra. By understanding these aspects, you'll be better prepared to demonstrate your qualifications and fit for the position.

The interview process for a Data Engineer position at Tech Mahindra is structured to assess both technical and interpersonal skills, ensuring candidates are well-rounded and fit for the role. The process typically consists of multiple rounds, each designed to evaluate different competencies.

1. Initial Screening

The first step in the interview process is an initial screening, which may include a resume review followed by a brief phone interview with a recruiter. During this stage, candidates are often asked to provide a self-introduction and discuss their relevant projects and experiences. This is an opportunity to highlight your background in data engineering, including any specific technologies or methodologies you have worked with.

2. Aptitude and Technical Assessment

Following the initial screening, candidates usually undergo an aptitude test that assesses logical reasoning, mathematical skills, and basic programming knowledge. This is often followed by a technical assessment, which may include coding challenges or questions related to data structures, algorithms, and specific programming languages such as SQL or Python. Candidates should be prepared to demonstrate their problem-solving abilities and coding proficiency.

3. Technical Interview

The technical interview is a critical component of the process, typically consisting of one or more rounds. In these interviews, candidates can expect to face questions that delve deeper into their technical expertise, including topics such as ETL processes, data modeling, and cloud technologies like Azure Data Factory and Databricks. Interviewers may also ask candidates to solve coding problems in real-time, so being comfortable with live coding is essential.

4. Project Discussion

In addition to technical skills, candidates will likely be asked to discuss their previous projects in detail. This includes explaining the technologies used, challenges faced, and the impact of their work. Interviewers are interested in understanding how candidates approach problem-solving and their ability to communicate complex technical concepts to non-technical stakeholders.

5. HR Interview

The final round typically involves an HR interview, where candidates discuss their motivations for applying, career goals, and fit within the company culture. This round may also cover behavioral questions to assess interpersonal skills and how candidates handle various workplace scenarios. Candidates should be prepared to discuss their salary expectations and any logistical considerations, such as notice periods.

As you prepare for your interview, it's important to familiarize yourself with the types of questions that may be asked in each of these rounds.

Here are some tips to help you excel in your interview.

Understand the Interview Structure

Tech Mahindra's interview process typically consists of multiple rounds, including aptitude tests, technical interviews, and HR discussions. Familiarize yourself with this structure and prepare accordingly. Expect at least three rounds, with the first two focusing on technical skills and the last one being HR-oriented. Knowing the flow will help you manage your time and energy effectively during the interview.

Prepare for Technical Questions

Given the emphasis on technical skills, be ready to answer questions related to data engineering principles, ETL processes, and specific tools like Azure Data Factory, SQL, and Python. Review your past projects in detail, as interviewers often ask about the technologies you used and the challenges you faced. Be prepared to write code or SQL queries on the spot, as practical assessments are common.

Highlight Relevant Experience

When discussing your projects, focus on your hands-on experience with data engineering tasks. Be specific about your role in the projects, the technologies you used, and the outcomes. This will demonstrate your capability and relevance to the position. If you have experience with cloud technologies or data visualization tools like Power BI, make sure to highlight that as well.

Emphasize Communication Skills

Tech Mahindra values strong communication skills, especially for roles that require interaction with non-technical stakeholders. Practice explaining complex technical concepts in simple terms. Be prepared to discuss how you have effectively communicated project updates or technical details to team members or clients in the past.

Be Ready for Behavioral Questions

The HR round will likely include behavioral questions. Prepare to discuss your motivations for applying, your career goals, and how you handle challenges or conflicts in a team setting. Use the STAR (Situation, Task, Action, Result) method to structure your responses, providing clear examples from your past experiences.

Stay Updated on Industry Trends

Tech Mahindra is focused on innovation and staying ahead in the tech landscape. Familiarize yourself with current trends in data engineering, cloud technologies, and data analytics. Being knowledgeable about industry developments will not only impress your interviewers but also help you understand how you can contribute to the company's goals.

Practice Problem-Solving

Expect to encounter problem-solving scenarios during the technical interviews. Practice coding challenges and data structure problems to sharpen your skills. Websites like LeetCode or HackerRank can be useful for this. Additionally, be prepared to discuss your thought process while solving these problems, as interviewers often assess your approach as much as the final answer.

Be Yourself and Stay Confident

Finally, remember to be yourself during the interview. Tech Mahindra values authenticity and a good cultural fit. Approach the interview with confidence, and don’t hesitate to ask questions about the company culture, team dynamics, or specific projects you might be working on. This shows your genuine interest in the role and the organization.

By following these tips, you can present yourself as a well-rounded candidate who is not only technically proficient but also a great fit for Tech Mahindra's collaborative and innovative environment. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Data Engineer interview at Tech Mahindra. The interview process typically includes technical assessments, project discussions, and HR evaluations. Candidates should focus on demonstrating their technical skills, problem-solving abilities, and understanding of data engineering principles.

Technical Skills

1. Can you explain the ETL process and its importance in data engineering?

Understanding the ETL (Extract, Transform, Load) process is crucial for a Data Engineer, as it is the backbone of data integration and management.

How to Answer

Discuss the stages of ETL, emphasizing how each stage contributes to data quality and accessibility. Mention any tools you have used, such as Azure Data Factory.

Example

“The ETL process is essential for transforming raw data into a usable format. In the extraction phase, data is gathered from various sources. During transformation, I apply business rules to clean and format the data. Finally, in the loading phase, the data is stored in a data warehouse for analysis. I have used Azure Data Factory to automate these processes, ensuring efficiency and accuracy.”

2. What is your experience with SQL and how do you optimize queries?

SQL is a fundamental skill for Data Engineers, and optimization is key to performance.

How to Answer

Highlight your experience with SQL, including specific examples of complex queries you’ve written. Discuss techniques you use for optimization, such as indexing or query restructuring.

Example

“I have extensive experience with SQL, having written complex queries for data extraction and reporting. To optimize queries, I often use indexing to speed up data retrieval and analyze execution plans to identify bottlenecks. For instance, I improved a report generation query by restructuring it to minimize joins, resulting in a 30% performance increase.”

3. Describe a project where you implemented data pipelines. What challenges did you face?

This question assesses your practical experience and problem-solving skills.

How to Answer

Provide a specific example of a project, detailing the technologies used and the challenges encountered. Focus on how you overcame those challenges.

Example

“In my last project, I built a data pipeline using Azure Data Factory to automate data ingestion from multiple sources. One challenge was handling data inconsistencies. I implemented data validation checks during the transformation phase, which significantly reduced errors and improved data quality.”

4. How do you ensure data quality and integrity in your projects?

Data quality is critical in data engineering, and interviewers want to know your approach.

How to Answer

Discuss the methods you use to maintain data quality, such as validation rules, testing, and monitoring.

Example

“I ensure data quality by implementing validation rules at various stages of the ETL process. I also conduct regular audits and use automated testing frameworks to catch discrepancies early. For instance, I set up alerts for data anomalies, which helped us identify and rectify issues before they impacted reporting.”

5. What is your experience with cloud technologies, specifically Azure?

Cloud technologies are increasingly important in data engineering roles.

How to Answer

Share your experience with Azure services, particularly those relevant to data engineering, such as Azure Data Factory, Azure Databricks, or Azure SQL Database.

Example

“I have worked extensively with Azure, particularly Azure Data Factory for orchestrating data workflows and Azure Databricks for processing large datasets. I utilized Databricks to run PySpark jobs for real-time data processing, which improved our data handling capabilities significantly.”

Data Modeling and Architecture

1. Can you explain the difference between a star schema and a snowflake schema?

Understanding data modeling is essential for structuring data effectively.

How to Answer

Define both schemas and discuss their use cases, emphasizing the advantages and disadvantages of each.

Example

“A star schema has a central fact table connected to dimension tables, making it simpler and faster for queries. In contrast, a snowflake schema normalizes dimension tables into multiple related tables, which can save space but may complicate queries. I prefer using a star schema for reporting purposes due to its efficiency in query performance.”

2. How do you approach data integration from multiple sources?

This question assesses your ability to handle diverse data environments.

How to Answer

Discuss your strategies for integrating data, including tools and techniques you use.

Example

“I approach data integration by first assessing the data sources and their formats. I use ETL tools like Azure Data Factory to extract data from various sources, ensuring I handle different data types appropriately. I also implement data transformation rules to standardize the data before loading it into a central repository.”

3. What are some common data quality issues you have encountered, and how did you resolve them?

This question evaluates your problem-solving skills in real-world scenarios.

How to Answer

Provide examples of data quality issues and the steps you took to resolve them.

Example

“I’ve encountered issues like duplicate records and missing values in datasets. To resolve these, I implemented deduplication algorithms and established data entry validation rules. For missing values, I used imputation techniques based on the data distribution, which improved the overall quality of our datasets.”

4. Describe your experience with data visualization tools. Which do you prefer and why?

Data visualization is key for presenting data insights effectively.

How to Answer

Share your experience with specific tools and your rationale for preferring one over another.

Example

“I have experience with Power BI and Tableau for data visualization. I prefer Power BI due to its seamless integration with Azure services and its user-friendly interface, which allows for quick report generation and dashboard creation. I’ve used it to create interactive dashboards that provide real-time insights to stakeholders.”

5. How do you handle performance tuning in data pipelines?

Performance tuning is crucial for efficient data processing.

How to Answer

Discuss the techniques you use to optimize data pipelines for performance.

Example

“I handle performance tuning by analyzing the execution times of various components in the data pipeline. I often use partitioning and parallel processing to speed up data ingestion and transformation. For instance, I partitioned a large dataset in Azure Data Lake, which reduced processing time by 40%.”

Behavioral and HR Questions

1. Tell me about a time you had to work with a difficult stakeholder. How did you manage the situation?

This question assesses your interpersonal skills and ability to handle conflict.

How to Answer

Provide a specific example, focusing on your communication and problem-solving skills.

Example

“I once worked with a stakeholder who had unrealistic expectations regarding project timelines. I scheduled a meeting to discuss their concerns and provided a detailed project plan, highlighting potential risks and realistic timelines. This open communication helped align our expectations and fostered a collaborative relationship.”

2. How do you prioritize your tasks when working on multiple projects?

This question evaluates your time management and organizational skills.

How to Answer

Discuss your approach to prioritization and any tools you use to manage your workload.

Example

“I prioritize tasks based on project deadlines and impact. I use project management tools like Jira to track progress and set reminders for critical tasks. This approach allows me to stay organized and ensure that I meet deadlines without compromising quality.”

3. What motivates you to work in data engineering?

This question helps interviewers understand your passion for the field.

How to Answer

Share your motivations and what excites you about data engineering.

Example

“I am motivated by the challenge of transforming raw data into actionable insights. The ability to solve complex problems and contribute to data-driven decision-making excites me. I enjoy working with cutting-edge technologies and continuously learning to improve my skills in this rapidly evolving field.”

4. How do you stay updated with the latest trends and technologies in data engineering?

This question assesses your commitment to professional development.

How to Answer

Discuss the resources you use to stay informed about industry trends.

Example

“I stay updated by following industry blogs, attending webinars, and participating in online courses. I also engage with the data engineering community on platforms like LinkedIn and GitHub, where I can learn from others and share my experiences.”

5. Where do you see yourself in five years?

This question helps interviewers gauge your career aspirations.

How to Answer

Share your career goals and how they align with the company’s direction.

Example

“In five years, I see myself in a senior data engineering role, leading projects and mentoring junior engineers. I aim to deepen my expertise in cloud technologies and data architecture, contributing to innovative solutions that drive business success at Tech Mahindra.”