KASHII UPDATEZ Everyday Student Requirements & Python Coding Tutorials by Python Kashi
REQUIREMENT_ID_1365 β€’ 3-DAY_ACTIVE_POLICY

Campus Drive – Multiple Technology Roles (Java, Python, Data Analytics, Data Science, AI/ML)

Company
Company Google
Type
Opportunity Type Internship
Salary
Stipend / Salary Not disclosed
Location
Location Pan India
Posted Date
Posted Date Sep 27, 2026
Java Python SQL Data Structures Object Oriented Programming Data Analytics Statistics Machine Learning Artificial Intelligence Data Visualization Database fundamentals
πŸ“–
Comprehensive placement paper repository Open Resource β†—
Curated set of past placement papers to help candidates practice typical questions asked by top tech firms.
πŸ“–
Interview experiences and process insights Open Resource β†—
Detailed accounts of recruitment stages, question patterns, and candidate tips for success.
πŸ“–
Preparation guide for tech interviews Open Resource β†—
Step‑by‑step guide covering coding, system design, and behavioral preparation for major tech companies.
πŸ“–
Extensive problem‑solving platform Open Resource β†—
Large collection of algorithmic problems to sharpen coding skills and improve speed and accuracy.

Graduates from recognized institutions with any of the following degrees: B.Tech, B.E., BCA, B.Sc., MCA, M.Sc. Minimum aggregate of 60% (or equivalent CGPA). No active backlogs at the time of application. Candidates from the 2023, 2024 or 2025 batch are eligible. Strong interest in technology, good communication skills, and basic programming knowledge are essential.

1
Round 1: Virtual Technical Interview
2
Round 2: Offline HR Interview
Codenera is a technology services firm that partners with global MNCs to deliver end‑to‑end software solutions. With a strong focus on innovation, Codenera has built a reputation for nurturing fresh talent and placing them on high‑impact projects for its multinational clients. The organization operates across major Indian metros and follows a collaborative, agile work culture that encourages continuous learning and rapid skill development. The Open Campus Drive is designed for fresh graduates who are eager to start their professional journey in software development, data analytics, data science, or artificial intelligence. Candidates can choose from five technology tracks – Java, Python, Data Analytics, Data Science, and AI/ML – each equipped with a well‑defined curriculum covering core concepts, tools, and real‑world project exposure. The drive promises a fast‑track entry into the industry, with the possibility of being placed directly with a leading MNC client based on performance and fit. **Key Responsibilities** 1. Participate in the design, development, and testing of applications or data pipelines as per the selected technology track. 2. Write clean, maintainable code following industry best practices and coding standards. 3. Collaborate with senior engineers and data scientists to understand business requirements and translate them into technical solutions. 4. Perform unit testing, debugging, and performance tuning of modules. 5. Assist in data extraction, cleaning, and visualization tasks for analytics projects. 6. Contribute to documentation, code reviews, and knowledge‑sharing sessions. 7. Stay updated with emerging technologies and suggest improvements to existing solutions. 8. Work in cross‑functional teams, communicating progress and challenges effectively. 9. Support deployment activities and post‑deployment monitoring. 10. Participate in continuous learning programs and certification courses offered by Codenera. **Technology Stack** - Java Track: Core Java, OOP, Collections, Multithreading, SQL, Database fundamentals, Java application development. - Python Track: Python programming, OOP, data structures, file handling, SQL, Python‑based applications. - Data Analytics Track: SQL, Excel, Python, Pandas, NumPy, data cleaning, visualization, statistical fundamentals. - Data Science Track: Python, Pandas, NumPy, statistics, ML fundamentals, EDA, model evaluation, visualization. - AI/ML Track: Python, ML fundamentals, supervised/unsupervised learning, model evaluation, AI basics, introductory deep learning, NLP. **Growth Path** Successful candidates can expect a clear progression from Associate Engineer to Senior Engineer, Lead, and eventually Solution Architect or Data Science Lead, depending on performance and domain expertise. Codenera invests heavily in upskilling through internal bootcamps, certifications, and mentorship, ensuring that freshers quickly become valuable contributors. **Why Join** - Direct exposure to a reputed MNC client and real‑world projects from day one. - Structured training across multiple high‑demand technology domains. - Competitive compensation and performance‑linked incentives. - A vibrant, inclusive culture that values innovation, collaboration, and work‑life balance. - Opportunities for rapid career growth and international assignments. Candidates who are passionate about technology, possess strong analytical abilities, and are eager to learn will find this drive an ideal launchpad for a rewarding career.

Google 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 🎯
Google Interview Preparation Corner

Comprehensive guide covering typical interview formats, common questions, and preparation tips for Google 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 (Java, Python, SQL, Data Structures, Object Oriented Programming, Data Analytics, Statistics, Machine Learning, Artificial Intelligence, Data Visualization, Database fundamentals) & 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
Google 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 Google as a Campus Drive – Multiple Technology Roles (Java, Python, Data Analytics, Data Science, AI/ML)?
Preparation Tip: Highlight Google's market reputation, recent tech innovations, and how your skills in Java 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.

More Fresh Requirements in Software Engineering

View Category Feed β†—
Google
Campus Drive – Multiple Technology Roles (Java, Python, Data Analytics, Data Science, AI/ML)
Apply Apply Now β†—
Chat Chat with Kashii