What is a Data Engineer at Mphasis?

As a Data Engineer at Mphasis, you are at the forefront of digital transformation, helping global enterprise clients modernize their data infrastructure. Mphasis partners with top-tier organizations across banking, financial services, logistics, and technology to build scalable, resilient, and highly available data ecosystems. In this role, you are not just writing code; you are building the foundational pipelines that enable data-driven decision-making at a massive scale.

Your impact directly influences how client businesses operate. By designing robust ETL/ELT pipelines, optimizing Big Data processing, and ensuring data quality, you empower analytics and machine learning teams to extract actionable insights. The work is fast-paced and highly applied, requiring you to bridge the gap between complex raw data and polished, business-ready datasets.

This position offers a unique blend of technical depth and consulting breadth. You will face diverse data challenges, varying from legacy system migrations to greenfield cloud-native architectures. Candidates who thrive here are those who possess strong core technical competencies—particularly in distributed processing and querying—and the adaptability to deliver results efficiently across different client environments.

Common Interview Questions

The questions you face at Mphasis will be highly specific and technical. The goal is not to memorize answers, but to recognize the pattern: interviewers want to see that you know the exact syntax and functions required to manipulate data day-to-day.

SQL and Database Fundamentals

This category tests your ability to retrieve and manipulate data using standard SQL. Expect questions that require you to write queries on the spot.

• Write a query to find the cumulative sum of sales per month.
• Explain the difference between UNION and UNION ALL. Which is faster and why?
• How do you find duplicate records in a table using SQL?
• Explain the concept of a Self Join and provide a scenario where you would use it.
• What are the different types of indexes, and how do they improve query performance?

PySpark Syntax and Operations

This category is heavily emphasized. You must know the pyspark.sql API inside and out.

• What is the syntax to drop duplicate rows in a PySpark DataFrame based on a specific subset of columns?
• How do you convert a string column to a timestamp column in PySpark?
• Explain the difference between repartition() and coalesce(). When would you use each?
• Write the PySpark syntax to group by a column and find the maximum value of another column.
• How do you read a CSV file into a PySpark DataFrame while inferring the schema and dropping malformed records?

Data Engineering and Architecture

These questions test your broader understanding of data systems and pipeline design.

• What is the difference between a Fact table and a Dimension table?
• How do you handle late-arriving data in a batch processing pipeline?
• Explain the concept of Slowly Changing Dimensions (SCD) Type 1 vs. Type 2.
• Describe a time you had to optimize a slow-running ETL pipeline. What steps did you take?
• What is the Parquet file format, and why is it preferred in Big Data processing?

Getting Ready for Your Interviews

Success in the Mphasis interview process requires a sharp focus on foundational data engineering skills and the ability to demonstrate your technical knowledge quickly and accurately. Your preparation should be targeted and practical.

Technical Syntax and Execution – Interviewers at Mphasis place a heavy emphasis on exact syntax and practical coding knowledge, particularly in PySpark and SQL. You will be evaluated on your ability to write clean, functional code without relying heavily on IDE auto-completion or external references.

Direct Problem-Solving – The focus is often on straightforward, applied technical questions rather than abstract, high-level logical puzzles. You must demonstrate that you can take a standard data transformation requirement and immediately translate it into the correct functions and queries.

Professionalism and Adaptability – Because you will often be deployed on critical client projects, interviewers look for candidates who remain composed, concise, and professional under pressure. You should be prepared to navigate varying interview styles, maintaining your focus on delivering accurate technical answers regardless of the conversation's pace.

Interview Process Overview

The interview process for a Data Engineer at Mphasis is typically streamlined, fast-paced, and highly focused on technical screening. In many cases, candidates are initially contacted by recruiting agencies or consultancy HR representatives who partner with Mphasis to source talent. After a brief initial screening to confirm your availability, experience, and tech stack alignment, you will be scheduled for the core technical evaluation.

You should expect a very direct technical round. Unlike companies that space out multiple behavioral, architectural, and logical rounds, Mphasis often consolidates its evaluation into one or two intensive technical interviews. These sessions are usually straightforward and heavily index on your immediate recall of core data engineering syntax. Interviewers tend to dive straight into technical questioning with minimal small talk, aiming to validate your hands-on coding capabilities as quickly as possible.

Because the process is so streamlined, the margin for error in the technical round is small. You must be prepared to answer rapid-fire questions about specific functions, transformations, and database operations. The pace can occasionally feel abrupt, so maintaining a calm, professional demeanor and providing concise, accurate answers is your best strategy for success.

The visual timeline above outlines the typical progression from the initial recruiter screen through the core technical evaluations. Use this to understand the velocity of the process; because it is often condensed into just a few steps, you must be technically prepared from the moment you agree to the first technical interview.

Deep Dive into Evaluation Areas

To succeed, your preparation must align with the specific technical areas Mphasis prioritizes. The evaluation is heavily weighted toward practical syntax and data manipulation rather than abstract system design.

SQL and Relational Data Manipulation

SQL is the bedrock of data engineering at Mphasis. Interviewers expect you to be fluent in complex querying, data aggregation, and performance optimization. This is not about basic SELECT statements; it is about proving you can manipulate large datasets efficiently.

Be ready to go over:

• Window Functions – Using ROW_NUMBER(), RANK(), DENSE_RANK(), and LEAD()/LAG() to solve complex analytical problems.
• Advanced Joins and Aggregations – Understanding the nuances of inner, outer, cross, and self joins, along with GROUP BY and HAVING clauses.
• Performance Tuning – Knowing how to read execution plans, use indexes effectively, and avoid common bottlenecks like Cartesian products.
• Advanced concepts (less common) – Recursive CTEs, pivoting/unpivoting data, and handling complex JSON or XML data within SQL.

Example questions or scenarios:

• "Write a SQL query to find the second highest salary in each department using window functions."
• "Explain the difference between RANK() and DENSE_RANK() with a practical data example."
• "How would you optimize a query that is joining two massive tables and running too slowly?"

PySpark and Big Data Processing

For modern data engineering roles at Mphasis, PySpark is heavily scrutinized. Based on candidate experiences, interviewers will ask highly specific questions about PySpark syntax, DataFrame operations, and built-in functions. You must know the code, not just the theory.

Be ready to go over:

• DataFrame Operations – Selecting, filtering, dropping, and renaming columns. Exact syntax is frequently tested.
• Transformations vs. Actions – Clear understanding of lazy evaluation and the difference between operations like map(), filter() (transformations) and collect(), count() (actions).
• PySpark SQL Functions – Utilizing pyspark.sql.functions for string manipulation, date formatting, and conditional logic (when().otherwise()).
• Advanced concepts (less common) – Broadcast variables, handling data skewness in partitions, and optimizing Spark memory management.

Example questions or scenarios:

• "What is the exact PySpark syntax to add a new column based on a conditional statement?"
• "Explain how you would handle missing or null values in a PySpark DataFrame."
• "Write the PySpark code to perform an inner join between two DataFrames and aggregate the results."

Core Data Engineering & ETL Concepts

While syntax is king, you must also demonstrate a solid understanding of how data moves from source to destination. You will be evaluated on your knowledge of ETL/ELT principles and data warehousing fundamentals.

Be ready to go over:

• Data Warehousing – Differences between Star and Snowflake schemas, and understanding of Fact and Dimension tables.
• Pipeline Architecture – High-level understanding of how to extract data from APIs or databases, transform it, and load it into a target destination.
• Data Quality – Techniques for ensuring data integrity, handling duplicates, and managing schema evolution.

Example questions or scenarios:

• "Describe the difference between an ETL and an ELT pipeline."
• "How do you handle slowly changing dimensions (SCD) in a data warehouse?"
• "What steps do you take to validate data quality after a large batch load?"

Key Responsibilities

As a Data Engineer at Mphasis, your day-to-day work revolves around building and maintaining the infrastructure that processes vast amounts of client data. You will spend a significant portion of your time writing and optimizing PySpark jobs and complex SQL queries to extract data from legacy systems, transform it to meet business logic, and load it into modern cloud data warehouses or data lakes.

You will frequently collaborate with client stakeholders, business analysts, and downstream data scientists. This requires you to translate business requirements into technical pipeline designs. You will also be responsible for monitoring pipeline health, troubleshooting failed jobs, and optimizing existing code to reduce processing time and compute costs.

Additionally, because Mphasis operates in an IT services model, you will often find yourself adapting to different client environments. One project might require heavy AWS Glue and EMR usage, while the next might focus on Azure Data Factory and Databricks. Flexibility, rapid onboarding to new tech stacks, and a commitment to delivering high-quality, documented code are essential components of your daily responsibilities.

Role Requirements & Qualifications

To be competitive for the Data Engineer role at Mphasis, candidates must possess a strong mix of core programming skills and data processing expertise.

• Must-have skills – Deep proficiency in SQL and Python (specifically PySpark). You must have hands-on experience building and scheduling batch or streaming ETL pipelines. Strong understanding of relational databases and data warehousing concepts is non-negotiable.
• Experience level – Typically, candidates need 3 to 7 years of dedicated data engineering experience, often with a background in software engineering, database administration, or BI development.
• Soft skills – Clear, concise communication is critical. You must be able to explain technical concepts quickly. Adaptability and professional resilience are also key, as client requirements and project environments can shift rapidly.
• Nice-to-have skills – Experience with major cloud platforms (AWS, Azure, or GCP), knowledge of orchestration tools like Apache Airflow, and familiarity with CI/CD pipelines for data deployments.

Frequently Asked Questions

Q: How long does the Mphasis interview process take? The process is generally very fast. Candidates often report experiencing just one or two technical rounds before a decision is made. The timeline from the initial recruiter call to the final technical round can be as short as a week.

Q: The interviewer didn't ask any behavioral or logical questions. Is this normal? Yes. Based on candidate feedback, Mphasis technical rounds can be highly focused on code syntax (especially PySpark and SQL). Interviewers often skip behavioral questions to maximize time spent on evaluating your hands-on coding knowledge.

Q: What should I do if the interviewer's style feels abrupt or rushed? Maintain your professionalism and composure. Some interviewers prefer a highly direct, rapid-fire approach and may keep the interview brief (sometimes as short as 10–15 minutes). Do not take this personally; focus on delivering clear, concise, and accurate technical answers.

Q: Will I be writing code in an IDE or on a whiteboard? You will typically be asked to share your screen and write code in an online editor, Notepad, or directly in the chat. You should be prepared to write syntactically correct code without the help of IDE auto-completion.

Q: Does Mphasis hire through external consultancies? Yes, it is very common to be contacted and scheduled by an external recruiting agency or consultancy HR on behalf of Mphasis. Ensure you communicate clearly with them, as they coordinate the logistics of your interview.

Other General Tips

• Brush Up on Exact Syntax: Do not rely on pseudocode. Because interviewers focus heavily on PySpark and SQL functions, spend the days before your interview reviewing the exact syntax for common data manipulations, aggregations, and window functions.

• Keep Answers Concise: If an interviewer is moving quickly, adapt to their pace. Give the direct technical answer first. If they want more detail or a logical breakdown of your approach, let them ask for it.

• Handle Ambiguity Professionally: If an interviewer introduces themselves briefly or skips standard introductions, take the high road. Remain polite, introduce yourself quickly, and pivot smoothly into the technical discussion. Your professionalism is quietly being evaluated.

• Vocalize Your Assumptions: If a SQL or PySpark question lacks specific details (e.g., how to handle nulls in a join), state your assumption out loud before writing the code. This shows attention to detail and data quality awareness.

Summary & Next Steps

Securing a Data Engineer role at Mphasis is an excellent opportunity to work on high-impact, enterprise-scale data solutions. The work you do will directly enable global clients to modernize their analytics and drive business value through data. By mastering the core technical skills required—specifically SQL and PySpark—you will position yourself as a strong asset to their consulting and delivery teams.

The compensation data above provides a general baseline for the role. Keep in mind that actual offers will vary based on your specific years of experience, your location, and the complexity of the client project you are being hired to support.

Your best strategy moving forward is to focus heavily on practical execution. Review your core syntax, practice writing queries without auto-complete, and prepare yourself mentally for a fast-paced, direct interview environment. Remember that confidence, concise communication, and professional adaptability are just as important as your technical knowledge. For more insights, practice scenarios, and detailed breakdowns of data engineering concepts, continue utilizing resources on Dataford. You have the skills to succeed—now focus on demonstrating them clearly and confidently.