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REQUIREMENT_ID_1457 • 3-DAY_ACTIVE_POLICY

SDE (Backend Engineer)

Company
Company KIWI
Type
Opportunity Type Full-Time Job
Salary
Stipend / Salary 12-20 LPA
Location
Location Bangalore, Karnataka
Posted Date
Posted Date Yesterday
Java Spring Boot Spring MVC REST API design PostgreSQL MySQL Redis Docker Kubernetes Git Linux CI/CD (GitHub Actions) Kafka/RabbitMQ AWS (EKS RDS S3) OpenTelemetry Grafana Problem Solving Data Structures & Algorithms Communication
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Graduation degree: B.E. or B.Tech (or equivalent) in Computer Science, Information Technology, or related engineering streams. Minimum CGPA/percentage: 6.5 CGPA (or 65%). Batch year: 2020‑2027. No active backlogs at the time of joining. Candidates must have 1‑4 years (SDE‑1) or 4+ years (SDE‑2) of professional backend development experience in Java.

1
Round 1: Online coding assessment (DSA & problem solving)
2
Round 2: Technical interview (system design, backend concepts, coding)
3
Round 3: HR interview (culture fit, compensation discussion)
Kiwi, operating under the brand Gokiwi Tech, is an emerging fintech startup focused on building a seamless digital payments ecosystem for Indian consumers and merchants. Founded by a group of ex‑banking and tech professionals, the company leverages modern cloud‑native architecture to deliver high‑throughput, low‑latency services such as UPI payments, card processing, and bill‑payment aggregation. With a growing user base across Tier‑1 and Tier‑2 cities, Kiwi is backed by prominent angel investors and is on a rapid hiring spree to scale its engineering team. The organization prides itself on a culture of ownership, rapid experimentation, and a strong emphasis on data‑driven decision making. The role of a Software Development Engineer (Backend) places you at the heart of Kiwi’s core product. You will join a tight‑knit backend engineering squad responsible for designing, building, and operating a fleet of Java micro‑services that run on Kubernetes. From day one you will be writing production‑grade code, shipping features end‑to‑end, and participating in on‑call rotations that give you real‑world exposure to high‑scale systems. The position is open for both SDE‑1 (1‑4 years experience) and SDE‑2 (4+ years experience) candidates, with responsibilities scaling with seniority. Key responsibilities include: 1. Develop and maintain Java (17+) micro‑services using Spring Boot and Spring MVC. 2. Design robust REST APIs, write database migrations, and implement comprehensive unit/integration tests. 3. Integrate with third‑party partners (payment gateways, KYC providers) handling callbacks, idempotency, retries, and reconciliation. 4. Optimize data access patterns on PostgreSQL/MySQL, detect and fix N+1 queries, and tune indexes. 5. Build background jobs and message‑driven workflows using Kafka/RabbitMQ. 6. Debug production incidents using distributed tracing, logs, metrics, and partner response analysis. 7. Participate in code reviews, contribute to CI/CD pipelines (GitHub Actions), and improve observability (OpenTelemetry, Grafana). 8. Mentor junior engineers, share best practices, and drive technical discussions on scalability and reliability. 9. Contribute to infrastructure automation on AWS (EKS, RDS, Secrets Manager) and container orchestration. 10. Write clear documentation, incident post‑mortems, and support on‑call duties. The tech stack revolves around Java 17, Spring ecosystem, PostgreSQL, Redis, Docker, Kubernetes, AWS services, and modern observability tools. Kiwi offers a clear growth path: high‑performing SDE‑1s can progress to SDE‑2 within 12‑18 months, followed by Lead Engineer and Architecture roles. The company encourages continuous learning, provides a budget for certifications, and hosts regular tech talks. Joining Kiwi means working on mission‑critical fintech products, gaining exposure to end‑to‑end payment flows, and collaborating with a passionate team that values innovation, ownership, and work‑life balance.

KIWI — Data Analytics & SQL Interview Guide

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

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

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MOCK TESTS & PAPERS 📝
Comprehensive Interview Prep Resources & Syllabus

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

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CODING PRACTICE 💻
Algorithm and Data Structure Problem Set

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

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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, Spring Boot, Spring MVC, REST API design, PostgreSQL, MySQL, Redis, Docker, Kubernetes, Git, Linux, CI/CD (GitHub Actions), Kafka/RabbitMQ, AWS (EKS, RDS, S3), OpenTelemetry, Grafana, Problem Solving, Data Structures & Algorithms, 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
KIWI 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 ▼
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 ▼
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 ▼
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 ▼
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 ▼
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 KIWI as a SDE (Backend Engineer)?
Preparation Tip: Highlight KIWI'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.

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