KPMG is Hiring Data Engineer | Finsplitz

Introduction

Are you a data enthusiast with a knack for building robust data pipelines and transforming raw data into actionable insights? KPMG, a leading global professional services firm renowned for its Audit, Tax, and Advisory services, is actively seeking Data Engineers for its various teams across India, primarily in major hubs like Bengaluru, Gurugram, Pune, Hyderabad, and Mumbai. In today’s data-driven world, KPMG leverages advanced analytics and data engineering to provide cutting-edge solutions to its diverse client base, ranging from multinational corporations to government entities. As a Data Engineer at KPMG, you will play a crucial role in designing, developing, and maintaining the firm’s data infrastructure, enabling data-driven decision-making, and contributing to innovative client engagements. This role offers a unique blend of technical challenge and business impact within a global consulting environment.

Roles and Responsibilities

A Data Engineer at KPMG is responsible for the end-to-end management of data, from ingestion to transformation and accessibility for analysis. The specific responsibilities can vary depending on whether the role is client-facing (Advisory/Consulting) or supports internal firm operations (Global Services/Technology).

Typical responsibilities for a Data Engineer at KPMG might include:

• Data Pipeline Design & Development:
  • Designing, building, and maintaining scalable and efficient data pipelines using ETL (Extract, Transform, Load) or ELT processes.
  • Developing data ingestion frameworks to collect data from various sources (databases, APIs, streaming data, flat files).
  • Implementing real-time processing solutions where required.
• Data Modeling & Warehousing:
  • Designing and implementing data models for data warehouses, data lakes, and other data stores.
  • Optimizing existing data models for performance and scalability.
  • Working with structured and unstructured data.
• Cloud Data Platform Utilization:
  • Leveraging cloud-native data services on platforms like Azure (Azure Data Factory, Databricks, Azure Data Lake), AWS, or GCP (Data Flow, Data Proc, BigQuery, Pub/Sub) for building and operationalizing data solutions.
  • Managing and optimizing cloud data storage and processing for performance and cost efficiency.
• Programming & Scripting:
  • Writing clean, efficient, and well-documented code in languages such as Python, Scala, Java, or Spark (PySpark) for data processing and transformation.
  • Developing SQL queries for data manipulation, analysis, and database interactions.
• Data Quality & Governance:
  • Implementing data quality checks and validation processes to ensure data accuracy and reliability.
  • Ensuring data governance policies are followed, including data lineage, classification, and security measures.
  • Collaborating with data governance teams to maintain data standards.
• Collaboration & Support:
  • Working closely with Data Scientists, Business Analysts, Solution Architects, and other stakeholders to understand data requirements and deliver appropriate data solutions.
  • Providing operational support for application codes and analytical models, often participating in support rotations.
  • Documenting data flows, architecture, and design specifications.
• Automation & Optimization:
  • Automating data processes and optimizing data flows for improved efficiency and reduced latency.
  • Troubleshooting data-related issues and ensuring data pipeline resilience.

Data Engineers at KPMG often work on a variety of projects across different industries, requiring adaptability and a continuous learning mindset.

Salary and Benefits

KPMG offers competitive salaries and a comprehensive benefits package for Data Engineers in India, reflecting their expertise in a high-demand field and their contribution to client value.

• Average Annual CTC (Cost to Company):
  • The average annual CTC for a Data Engineer at KPMG in India is approximately ₹18.2 lakhs per annum.
  • Salaries generally range from ₹6.6 lakhs (for entry-level Software Engineer – Data Engineer roles with 3 years experience) up to ₹37.9 lakhs per annum or even higher for more experienced professionals (e.g., Senior Software Engineer, Consultant, Manager Data Engineer).
  • For Associate Software Engineer (Data Engineer) roles, average CTC can be around ₹12.1 lakhs to ₹15.6 lakhs (for candidates with 4-5 years of experience, but fresher salaries would be at the lower end of the range, potentially starting around ₹6-10 lakhs depending on skills and educational background).
  • For Senior Software Engineer (Data Engineer) roles (4+ years experience), salaries can range from ₹19.3 lakhs to ₹27.5 lakhs per annum.
  • Salaries vary significantly based on experience level, specific technical skills (e.g., niche cloud expertise), interview performance, and location (Bengaluru, Gurugram, Mumbai often have higher compensation).
• Comprehensive Benefits and Perks: KPMG provides a robust set of benefits designed to support employees’ professional and personal well-being.
  • Health & Wellbeing: Comprehensive medical insurance (Mediclaim), Group Term Life insurance, and Accident insurance coverage for employees and their families. This includes a wellbeing program with access to counselors for mental health and other life challenges.
  • Financial Security: Competitive base salary, Provident Fund (PF), Gratuity, and a performance-based bonus scheme that reflects firm and individual performance.
  • Flexible Work Options: KPMG values work-life balance and offers flexible working arrangements, including variations to office hours, part-time work, and work-from-home options depending on role and project needs.
  • Learning & Development: Significant investment in continuous learning. Access to the “KPMG Learning Academy” with a robust library of web-based training courses, instructor-led sessions, knowledge-sharing gateways, and support for professional certifications. Opportunities for job rotations within India and globally.
  • Career Progression: Clear career pathways from Associate to Consultant, Assistant Manager, Manager, and beyond. Structured appraisal systems and dedicated performance managers support career objectives and growth.
  • Personal Time-Off: Progressive leave benefits including shared leave, primary caregiver leave, adoption maternity leave, and even sabbatical leave options, depending on eligibility.
  • Work Environment & Culture: A collaborative and inclusive culture that fosters trust, mutual respect, and continuous improvement. Opportunities to work with diverse teams and contribute to impactful projects.

Eligibility Criteria

KPMG looks for Data Engineers who possess a strong technical foundation in data technologies, a problem-solving mindset, and a commitment to delivering high-quality solutions.

• Educational Qualification:
  • Bachelor’s degree in Computer Science, Information Technology, Engineering, Statistics, Mathematics, or a related quantitative field. A Master’s degree is often preferred for more senior roles or specific specializations.
  • Strong academic credentials are typically preferred, especially for fresh graduate roles.
• Experience:
  • For entry-level/Associate Data Engineer roles, fresh graduates with strong academic projects, relevant internships, or a foundational understanding of data engineering concepts are considered (0-2 years of experience).
  • For Consultant/Senior Associate roles, 3-7 years of relevant experience in designing, building, and maintaining data pipelines is usually required.
  • For Managerial roles, 7+ years of extensive experience is expected.
• Key Technical Skills (Essential & Desirable):
  • Programming Languages: Strong proficiency in at least one, and preferably more, of the following: Python (highly preferred), Scala, Java.
  • SQL: Expert-level SQL skills for complex querying, data manipulation, and performance optimization on various database systems (e.g., MS SQL Server, PostgreSQL, MySQL).
  • Big Data Technologies: Experience with technologies like Apache Spark (PySpark, Scala Spark) for distributed data processing, Hadoop ecosystem components (Hive, HDFS) is highly desirable.
  • Cloud Data Platforms: Hands-on experience with at least one major cloud platform’s data services:
    • Azure: Azure Data Factory, Azure Databricks, Azure Data Lake Storage, Azure Synapse Analytics.
    • AWS: S3, EMR, Glue, Redshift, Lambda, Kinesis.
    • GCP: BigQuery, Dataflow, Dataproc, Cloud Storage, Pub/Sub.
  • ETL/ELT Tools: Experience with traditional ETL tools (e.g., SSIS, Informatica) or modern cloud-native ETL/orchestration tools.
  • Data Warehousing/Lakes: Understanding of data warehousing concepts, data modeling (dimensional modeling, Kimball/Inmon), and data lake architectures (e.g., Snowflake, Redshift, Hive).
  • Version Control: Proficiency with Git or other version control systems.
  • Data Governance & Quality: Awareness of data governance principles, data quality frameworks, and metadata management.
• Key Soft Skills:
  • Analytical & Problem-Solving: Excellent analytical and logical reasoning skills to understand complex business problems and design effective data solutions.
  • Communication: Strong verbal and written communication skills to articulate technical concepts to both technical and non-technical stakeholders. Ability to engage with clients effectively.
  • Collaboration & Teamwork: Ability to work effectively in a team-oriented, collaborative environment, often across multiple projects.
  • Client-Facing Acumen: For consulting roles, the ability to understand client requirements, manage expectations, and present solutions clearly.
  • Adaptability & Learning Agility: Eagerness to learn new technologies, adapt to evolving project requirements, and work in a dynamic consulting environment.
  • Attention to Detail: Meticulous attention to detail for data accuracy and pipeline robustness.

Application Process

The application process for Data Engineer roles at KPMG in India is typically structured to assess technical proficiency, problem-solving abilities, and fit with the firm’s culture.

1. Online Application:
   • Candidates apply through KPMG’s official careers website (kpmg.com/in/en/careers.html), major job portals (LinkedIn, Naukri), or through campus recruitment drives.
   • Submit a detailed resume highlighting technical skills, relevant projects, academic achievements, and any internships.
2. Resume Screening:
   • HR and the recruiting team review applications to shortlist candidates whose profiles align with the job requirements.
3. Online Assessment (Potential):
   • For some roles, especially for freshers or high-volume hiring, an online assessment may be conducted. This typically includes sections on:
     • Aptitude: Logical reasoning, quantitative aptitude.
     • Verbal Ability: English comprehension and grammar.
     • Technical/Coding: May include coding challenges (DSA, SQL queries), or multiple-choice questions on data engineering concepts.

Interview Process

Candidates who clear the online assessment (if applicable) proceed to multiple rounds of interviews, which combine technical deep-dives with behavioral assessments. Typically, there are 3-5 rounds.

• Round 1: HR Screening / Initial Phone Interview (30-45 minutes)
  • Focus: Assess basic communication skills, understanding of the Data Engineer role, motivation to work at KPMG, career aspirations, and cultural fit. Discuss salary expectations and location preferences.
  • Questions: “Tell me about yourself,” “Why KPMG?”, “Why Data Engineering?”, “Are you comfortable with consulting firm dynamics (potentially client-facing, varied projects)?”
• Round 2: Technical Interview – Data Engineering Fundamentals (60-90 minutes)
  • Focus: This round is a deep dive into core data engineering concepts, programming skills, and SQL proficiency.
  • Questions:
    • SQL: Write complex SQL queries (joins, subqueries, window functions, aggregations), discuss database normalization, indexing, query optimization.
    • Programming (Python/Scala/Java): Coding questions (DSA – medium level), object-oriented programming concepts, functional programming concepts (if Scala/Spark).
    • Data Warehousing/Lakes: Concepts, different types of schemas (star, snowflake), ETL vs. ELT, data modeling.
    • Big Data: Basic understanding of Spark architecture, RDDs/DataFrames/Datasets, common transformations/actions.
    • Linux/Shell Scripting: Basic commands and scripting for automation.
    • Discussion of projects from your resume, focusing on your contributions and technical challenges.
• Round 3: Technical Interview – Cloud & System Design (60-90 minutes)
  • Focus: This round assesses your experience with cloud data platforms and your ability to design data solutions.
  • Questions:
    • Cloud Data Services: In-depth questions on services from one or more cloud platforms (e.g., Azure Data Factory, Databricks, Synapse; AWS Glue, Redshift, EMR; GCP BigQuery, Dataflow).
    • Data Pipeline Design: Design a data pipeline for a given scenario (e.g., real-time analytics, batch processing, streaming data). Discuss components, tradeoffs, scalability, and fault tolerance.
    • Troubleshooting: Scenario-based questions on debugging data pipeline failures or performance issues.
    • Data Governance: How would you ensure data quality and security in a given data ecosystem?
• Round 4: Managerial / Case Study Round (60-90 minutes)
  • Focus: This round evaluates your problem-solving approach in a business context, your ability to translate technical solutions into business value, and your potential for client interaction (if applicable).
  • Questions:
    • Case Study: You might be given a business problem involving data and asked to outline an approach, potential data sources, technologies, and challenges.
    • Behavioral: “Tell me about a time you faced a challenging technical problem and how you overcame it,” “How do you manage conflicting priorities?”, “Describe a time you collaborated effectively with a non-technical team.”
    • Discussion on your understanding of KPMG’s services and how Data Engineering fits in.
• Round 5: Partner / Senior Leadership / HR Round (30-60 minutes)
  • Focus: This final round is often with a Senior Manager, Director, or Partner. It assesses your overall fit with KPMG’s culture, leadership potential, long-term aspirations, and professionalism.
  • Questions: “Where do you see yourself in 5 years at KPMG?”, “What motivates you?”, “How do you deal with ambiguity or changing client requirements?”, “Why should we hire you over other candidates?”, “Any questions for me?” Salary and benefits discussion may also happen here.

Preparation Tips:

• Strengthen Fundamentals: Be strong in Data Structures & Algorithms, SQL, and at least one programming language (Python is highly recommended).
• Deep Dive into Cloud: Choose one cloud platform (Azure, AWS, or GCP) and understand its data engineering services thoroughly. Practice building sample pipelines.
• Big Data Concepts: Get a solid understanding of Spark, Hadoop, and distributed computing principles.
• Practice System Design: Work through common data pipeline design scenarios. Consider scalability, reliability, fault tolerance, and cost.
• Behavioral Skills: Prepare stories using the STAR method for common behavioral questions. Emphasize teamwork, problem-solving, and communication.
• Research KPMG: Understand KPMG’s core services, its technology/data initiatives, and its values. This will help you tailor your answers and ask informed questions.
• Consulting Mindset: If applying for advisory roles, understand the consulting model – client interaction, project-based work, and delivering solutions.
• Be Prepared to Learn: Demonstrate a strong eagerness to learn new technologies and adapt to different project contexts.

Conclusion

A Data Engineer role at KPMG in India offers a dynamic and intellectually stimulating career path. You’ll have the opportunity to work on diverse projects across various industries, leverage cutting-edge data technologies, and contribute directly to solving complex business challenges for clients. With a strong focus on professional development and a collaborative environment, KPMG provides an excellent platform for Data Engineers to grow their expertise and make a significant impact in the evolving data landscape.