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REQUIREMENT_ID_1751 β€’ 3-DAY_ACTIVE_POLICY

Associate Data Analytics Engineer

Company
Company PwC
Type
Opportunity Type Full-Time Job
Salary
Stipend / Salary 6-8 LPA
Location
Location Bangalore, India
Posted Date
Posted Date Today
Python SQL Spark Databricks Azure Delta Lake Lakehouse Data Warehouse Git CI/CD Data Modeling Data Pipeline Problem Solving Communication
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Aptitude Practice for Technical Interviews Open Resource β†—
Provides a wide range of aptitude questions to sharpen analytical and problem‑solving skills essential for data engineering interviews.
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PwC Recruitment Process Insights Open Resource β†—
Offers detailed walkthroughs of PwC’s hiring stages, helping candidates prepare for each interview round.
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PwC Interview Preparation Guide Open Resource β†—
Contains sample questions and strategies tailored to PwC’s interview format, useful for data engineering roles.
πŸ“–
Coding Challenges for Data Engineers Open Resource β†—
Features coding problems that test programming and algorithmic skills relevant to data engineering positions.

Bachelor’s degree in Computer Science, Information Technology, Engineering, or a related field. 1–3 years of relevant experience in data engineering or software development. Minimum 60% marks in the final year or equivalent. No backlogs allowed. Candidates should be recent graduates or within 3 years of graduation.

1
Round 1: Technical coding (Python/SQL)
2
Round 2: Technical interview (Data Engineering concepts)
3
Round 3: HR interview
PwC Acceleration Center India is a global hub that brings together world‑class advisory, assurance, tax and business services to deliver innovative solutions for clients across multiple geographies. The center focuses on digital transformation, leveraging advanced analytics, cloud technologies and AI to help clients stay ahead in a rapidly changing business environment. With a culture of collaboration, continuous learning and a commitment to diversity, PwC offers a platform where fresh talent can grow into industry leaders. The Data Engineer – Associate role is designed for recent graduates or early‑career professionals who are passionate about building scalable data solutions. As an associate, you will work closely with senior engineers and client teams to design, develop and maintain data pipelines that power analytics and decision‑making. Your responsibilities will include coding in Python and SQL, orchestrating Spark jobs on Databricks, and deploying cloud‑based data lakes and warehouses on Azure. You will also contribute to emerging GenAI initiatives, applying retrieval‑augmented generation techniques to enhance data products. Key responsibilities: 1. Design and develop end‑to‑end data pipelines using Python, Spark and Databricks. 2. Build and maintain Azure‑based data lakes, lakehouses and data warehouses. 3. Implement Delta Lake and Delta Live Tables for reliable data ingestion. 4. Collaborate with stakeholders to translate business requirements into technical specifications. 5. Perform data modeling, schema design and performance tuning. 6. Write unit tests, conduct debugging and troubleshoot production issues. 7. Use Git, CI/CD pipelines and best practices for code quality. 8. Participate in GenAI and RAG projects under guidance. 9. Continuously learn new tools and improve engineering practices. 10. Mentor junior team members and share knowledge. Tech stack: Python, SQL, Spark, Databricks, Azure Data Factory, Azure Synapse, Delta Lake, Lakehouse, Git, CI/CD. Growth path: Starting as an associate, you can progress to Senior Data Engineer, Lead Engineer, and eventually move into technical leadership or consulting roles within PwC’s global network. Why join PwC? The firm offers competitive compensation, a supportive learning environment, exposure to high‑impact projects, and a global career trajectory. Employees benefit from mentorship, structured training, and the opportunity to work with cutting‑edge technologies while contributing to meaningful business outcomes. Overall, this role is ideal for data enthusiasts who want to build a solid foundation in data engineering, work on real‑world challenges, and grow within a respected global organization.

PwC β€” Data Analytics & SQL Interview Guide

Previously asked questions, exam syllabus, coding benchmarks & round strategy.

APTITUDE & LOGIC πŸ“–
Aptitude Practice Questions & Online Mock Tests

Curated logical, quantitative, and verbal reasoning problems to sharpen reasoning skills required for the initial screening test.

Open Resource β†—
COMPANY GUIDE 🎯
PwC Interview Preparation Corner

Comprehensive guide covering typical interview formats, common questions, and preparation tips for PwC and off-campus tech roles.

Open Resource β†—
MOCK TESTS & PAPERS πŸ“
Comprehensive Interview Prep Resources & Syllabus

Collection of previous year questions, company-specific test patterns, and interview experiences for technical and HR rounds.

Open Resource β†—
CODING PRACTICE πŸ’»
Algorithm and Data Structure Problem Set

Practice problems to improve coding proficiency and algorithm problem-solving speed for technical rounds.

Open Resource β†—
Round 1: Online Assessment (OA)
Aptitude, Quantitative Logic & 2 Coding Problems
Focus on accuracy and speed. Practice arrays, strings, and standard arithmetic puzzles.
Round 2: Technical Interview 1
Core Tech Stack (Python, SQL, Spark, Databricks, Azure, Delta Lake, Lakehouse, Data Warehouse, Git, CI/CD, Data Modeling, Data Pipeline, Problem Solving, Communication) & Live Code Tracing
Explain your thought process aloud. Analyze time & space complexities before coding.
Round 3: System Design & Problem Solving
Database Schemas, APIs & Architecture Basics
Clarify edge cases, diagram schemas cleanly, and discuss scalability trade-offs.
Round 4: HR & Cultural Fit Discussion
PwC Core Values, Learning Agility & Offer Terms
Demonstrate passion, strong communication, and readiness for full-time collaboration.
What is the difference between WHERE and HAVING clauses in SQL? Answer β–Ό
Model Answer: WHERE filters rows before any groupings are applied, while HAVING filters aggregated groups after GROUP BY has executed.
Explain SQL Window functions: ROW_NUMBER(), RANK(), and DENSE_RANK(). Answer β–Ό
Model Answer: ROW_NUMBER() assigns unique sequential integers. RANK() assigns identical ranks to ties and skips ranks. DENSE_RANK() assigns identical ranks to ties without skipping rank numbers.
How do you handle NULL and missing values during data cleaning in Python/Pandas? Answer β–Ό
Model Answer: Use .isna().sum() to identify missing values. Impute with mean/median using .fillna() or remove with .dropna(subset=[...]) depending on variance impact.
What is the difference between Star Schema and Snowflake Schema in Data Warehousing? Answer β–Ό
Model Answer: Star Schema has denormalized dimension tables directly connected to the central Fact table. Snowflake Schema normalizes dimension tables into sub-dimensions to minimize redundancy.
How do you calculate MoM (Month-over-Month) growth in SQL? Answer β–Ό
Model Answer: Use LAG(revenue, 1) OVER (ORDER BY month) to fetch the previous month's revenue and compute (revenue - prev_revenue) / prev_revenue * 100.
Why do you want to join PwC as a Associate Data Analytics Engineer?
Preparation Tip: Highlight PwC's market reputation, recent tech innovations, and how your skills in Python directly solve their team's objectives.
Describe a challenging bug or academic project roadblock and how you resolved it.
Preparation Tip: Use the STAR method: Situation (project context), Task (what needed solving), Action (specific tools/logic applied), Result (quantifiable positive outcome).
How do you handle strict deadlines or sudden scope changes?
Preparation Tip: Explain your prioritization strategy, proactive communication with mentors/peers, and agile mindset.

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