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SDE I - Java

Flexiple

  • Bengaluru, India
  • Competitive Package
  • 5–4 Years
  • Posted Yesterday
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About the role

Hiring for Swiggy — via Flexiple, the recruiting partner on this role.

About the Company

Swiggy is India's leading on-demand convenience platform, spanning food delivery, Instamart quick commerce, Dineout and more. Its engineering team builds the real-time systems behind millions of daily orders, and Swiggy is built on microservices with Java as its core backend language. Swiggy is adding backend engineers in Bengaluru to keep these services fast and reliable as the business scales.

About the Role

As an SDE I on the backend team, you'll build and own Java and Spring Boot microservices used by millions of customers, partners and delivery executives. You'll ship APIs, improve performance and reliability, and work closely with engineering and cross-functional teams. This is a hybrid role based in Bengaluru, with 2–3 days a week in the office.

What you'll do

  • Build backend components and microservices in Java and Spring Boot
  • Design and implement RESTful APIs and integrations across Swiggy's services
  • Tune Java applications (JVM, multithreading, caching) to improve response times and stability
  • Maintain, refactor and troubleshoot backend codebases for high availability
  • Take part in code reviews, deployments and service monitoring
  • Work with product and cross-functional teams, and suggest Java-related improvements

What you bring

  • 1.5–4 years of professional software engineering experience, with at least 1 year of backend development in Java and Spring Boot
  • A solid grasp of REST APIs, microservices architecture and backend system design
  • Hands-on experience with performance tuning, multithreading and JVM optimisation
  • Familiarity with CI/CD, Git and cloud-native practices
  • You have shipped a Spring Boot service or API to production and supported it after release
  • Clear communication about API behaviour, and comfort moving quickly in a dynamic environment

Nice to have

  • Experience with Kafka, Redis, SQL/NoSQL databases, AWS, Docker or Kubernetes
  • Backend work at a consumer-internet or high-traffic product company
  • Immediate availability or a short notice period

Originally posted on: 3 Nov 2026

Eligibility & process

Who can apply

Seniority: Associate. Employment type: Full-time

Skills & stack

Java SQL Spring Boot REST APIs Microservices Redis AWS Docker

Selection process

Details are shared by the recruiter after shortlisting.

Prep kit

20 resources
Study material 4
Company-specific guides 4
Likely coding questions 4
Round-wise strategy 4
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, SQL, Spring Boot, REST APIs, Microservices, Redis, AWS, Docker) & 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 Flexiple Core Values, Learning Agility & Offer Terms Demonstrate passion, strong communication, and readiness for full-time collaboration.
Technical questions 5
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.
Behavioural & HR questions 3
Why do you want to join Flexiple as a SDE I - Java?
Highlight Flexiple'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.
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?
Explain your prioritization strategy, proactive communication with mentors/peers, and agile mindset.
Flexiple Competitive Package
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