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REQUIREMENT_ID_191 β€’ 3-DAY_ACTIVE_POLICY

Data Analyst/Engineer

Company
Company Black Box
Type
Opportunity Type Full-Time Job
Salary
Stipend / Salary 8- 10 LPA
Location
Location India
Posted Date
Posted Date Yesterday
Python SQL PostgreSQL Apache Airflow Apache Flink Pandas ETL Data Pipelines Linux Git Cloud basics PostGIS
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Aptitude Practice for Data Roles Open Resource β†—
Build logical reasoning and quantitative skills essential for data engineering interviews.
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Interview Preparation Guide Open Resource β†—
Learn common interview questions and best practices for technical and behavioral rounds.
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Job Search Resources Open Resource β†—
Explore job listings, company reviews, and interview experiences to prepare effectively.
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Coding Challenge Repository Open Resource β†—
Practice coding problems to sharpen algorithmic thinking for coding interviews.

Bachelor’s or Master’s degree in Computer Science, Information Technology, Data Science, Mathematics, or related fields. Minimum 60% marks or 6.5 CGPA. No backlog allowed. Recent graduates from any batch year are welcome.

1
Round 1: Technical (Coding + SQL)
2
Round 2: System Design & Data Engineering concepts
3
Round 3: HR
Black Box is a fast‑growing technology firm headquartered in Navi Mumbai, focusing on delivering innovative data solutions to a diverse set of clients across finance, retail, and healthcare. With a culture that champions experimentation and continuous learning, the company has built a reputation for turning complex data challenges into scalable products. The team is small but highly collaborative, and every member is encouraged to contribute ideas that shape the product roadmap. The Associate – Data Engineer role is designed for recent graduates who are passionate about data and eager to transition from academic projects to production‑grade engineering. As an associate, you will work closely with senior engineers to design, build, and maintain robust data pipelines that feed analytics and machine learning models. You will get hands‑on exposure to modern data infrastructure, including orchestration with Apache Airflow, stream processing with Apache Flink, and data storage in PostgreSQL. Key Responsibilities: 1. Develop and maintain ETL scripts to extract, transform, and load data from multiple sources. 2. Design and implement PostgreSQL schemas, write efficient SQL queries, and optimize database performance. 3. Build and schedule Airflow DAGs to automate data workflows. 4. Collaborate on real‑time data streaming solutions using Apache Flink. 5. Write clean, well‑documented Python code and perform unit testing. 6. Monitor pipeline health, troubleshoot failures, and implement alerts. 7. Participate in code reviews and share best practices. 8. Keep abreast of emerging data engineering tools and techniques. 9. Contribute to documentation and knowledge sharing sessions. 10. Assist in setting up CI/CD pipelines for data workflows. Tech Stack: Python, SQL, PostgreSQL, Apache Airflow, Apache Flink, Pandas, Git, Linux, basic cloud services (AWS/GCP/Azure). Growth Path: Starting as an associate, you can progress to Senior Data Engineer, Lead Data Engineer, and eventually to Data Engineering Manager or Head of Data Platform, depending on performance and leadership skills. Why Join: Black Box offers a dynamic work environment where your ideas directly influence product development. You will receive mentorship from seasoned engineers, access to learning resources, and the opportunity to work on high‑impact projects that shape the company’s data strategy. The compensation package is competitive, and the company values work‑life balance, encouraging flexible working arrangements where possible.

Black Box 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 🎯
Black Box Interview Preparation Corner

Comprehensive guide covering typical interview formats, common questions, and preparation tips for Black Box 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, PostgreSQL, Apache Airflow, Apache Flink, Pandas, ETL, Data Pipelines, Linux, Git, Cloud basics, PostGIS) & 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
Black Box 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 Black Box as a Data Analyst/Engineer?
Preparation Tip: Highlight Black Box'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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