L #2641
Applications open
Full-Time Job
Specialist - Python
Linedata
About the role
About Us
WHY LINEDATA ?
Join us in shaping the fintech of tomorrow.
With more than 1,350 colleagues across 20 locations worldwide, you'll help build cutting-edge platforms and tailored services that power the daily operations of 700 leading financial institutions. As a global, multicultural company with over 45 nationalities represented and just as many languages spoken, we thrive on diverse perspectives and collaboration. Together, we tackle future-focused topics like Al and the digitalization of finance, delivering smart solutions for our customers.
Whether you're just beginning your career or are an experienced professional, Linedata offers exposure to large-scale client projects, internal mobility and opportunities to develop your skills alongside leading experts.
Job Summary
We are looking for a skilled Python developer with hands-on experience in data manipulation and analysis using Pandas. The ideal candidate should have strong problem-solving skills and experience working with large datasets in real-world applications.
Key Responsibilities
- Write efficient, scalable Python code for data processing and transformation.
- Perform complex data manipulation using Pandas, NumPy, and other scientific libraries.
- Clean,transform, and analyze large volumes of structured and unstructured data.
- Develop scripts and tools to automate workflows.
- Collaborate with data engineers and analysts to optimize data pipelines.
- Participate in code reviews and follow best practices in version control (Git).
Required Skills
- Strong proficiency in Python, especially with Pandas and NumPy.
- Experience working with data files in formats like CSV, Excel, JSON, and SQL.
- Familiarity with Jupyter Notebooks, API integrations, and data visualization tools(e.g., Matplotlib, Seaborn).
- Experience with SQL queries and relational databases.
- Good understanding of software development lifecycle and version control (Git).
Good To Have
- Exposure to machine learning libraries (e.g., scikit-learn).
- Experience with cloud platforms (AWS/Azure/GCP) or data lake frameworks.
- Familiarity with Airflow, Spark, or other data orchestration tools.
Eligibility & process
Who can apply
Employment type: Full-time
Skills & stack
Selection process
Details are shared by the recruiter after shortlisting.
Prep kit
20 resourcesStudy material 4
Aptitude Practice Questions & Mock Tests
Curated logical, quantitative, and verbal reasoning problems for the initial online screening round.
Company-Specific Interview Preparation Corner
Detailed interview experiences, exam formats, and previous test questions for Linedata and tech roles.
Technical Placement Question Bank
High-yield coding cheat sheets, core CS fundamentals (OOP, DBMS, OS, Networks), and rapid revision guides.
Algorithm, DSA & Live Code Debugger Practice
Hands-on problem sets to improve coding speed and step-by-step memory debugging.
Company-specific guides 4
APTITUDE & LOGIC
Aptitude Practice Questions & Online Mock Tests
Curated logical, quantitative, and verbal reasoning problems to sharpen reasoning skills required for the initial screening test.
COMPANY GUIDE
Linedata Interview Preparation Corner
Comprehensive guide covering typical interview formats, common questions, and preparation tips for Linedata and off-campus tech roles.
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.
CODING PRACTICE
Algorithm and Data Structure Problem Set
Practice problems to improve coding proficiency and algorithm problem-solving speed for technical rounds.
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 (Python, SQL, AWS, Azure, GCP, Git, Machine Learning, Advanced Excel) & 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
Linedata 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 Linedata as a Specialist - Python?
Highlight Linedata's market reputation, recent tech innovations, and how your skills in Python directly solve their team's objectives.
Highlight Linedata'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.
Use the STAR method: Situation (project context), Task (what needed solving), Action (specific tools/logic applied), Result (quantifiable positive outcome).
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.
Explain your prioritization strategy, proactive communication with mentors/peers, and agile mindset.
Competitive Package
Mumbai, India
Employment type: Full-time
