JD #2403
Applications open
Full-Time Job
Data Engineer
Axa
About the role
AXA XL, a leading global property and casualty insurer, is part of the AXA Group and is known for its innovative approach to risk management. With a presence in over 100 countries, AXA XL leverages data, technology, and analytics to deliver tailored solutions to midβsized companies, multinationals, and highβnetβworth individuals. The companyβs culture is built around agility, inclusion, and a commitment to sustainability, reflected in its "Roots of Resilience" strategy that focuses on protecting natural ecosystems, addressing climate change, and embedding ESG principles into every business function.
The Databricks Developer role is a key position within the Global Data Platform (GDP) team of AXA Group Risk Management (GRM). The primary responsibility is to design, develop, and maintain data processing solutions on the Databricks platform, converting legacy applications into modern Python/R and SQL code. The role requires close collaboration with architects, product owners, and data engineering teams to industrialise pipelines, optimise performance, and ensure robust testing and documentation. As part of the GRM tech/data/AI pool, the developer will contribute to the transformation of legacy systems for the 2026β2027 period and beyond, ensuring that GRM can continue to deliver critical risk management insights.
Key responsibilities include:
1. Developing and maintaining data pipelines in Python, R, and SQL on Databricks.
2. Migrating legacy applications to the GDP platform.
3. Optimising code for performance and scalability.
4. Writing unit, integration, and regression tests.
5. Participating in code reviews and enforcing best practices.
6. Documenting technical specifications and user guides.
7. Providing support to endβusers and troubleshooting production issues.
8. Collaborating with crossβfunctional teams to align on data governance and quality.
9. Contributing to continuous integration/continuous deployment (CI/CD) workflows.
10. Staying updated on emerging data platform technologies.
Tech stack: Databricks, PySpark, Delta Lake, Azure Data Platform, Azure DevOps, Git, SQL, Python, R, Data Quality & DataOps tools.
Growth path: Starting as a junior developer, one can progress to Senior Data Engineer, Lead Architect, or Data Science Manager within the GRM data ecosystem, with opportunities to work on global projects and influence risk analytics strategy.
Why join AXA XL? The company offers a collaborative, inclusive environment, competitive compensation, and a strong focus on professional development. Employees benefit from flexible working arrangements, a culture that values workβlife balance, and a commitment to sustainability and social responsibility. AXA XLβs reputation for innovation and its global footprint provide a platform for career growth and meaningful impact.
Eligibility & process
Who can apply
Bachelorβs degree in Computer Science, Information Technology, Engineering, or related field. Minimum 60% (or 6.0 CGPA) in the qualifying exam. No backlog allowed. Fresh graduates are welcome. Prior internship or project experience in data engineering or analytics is a plus.
Skills & stack
Selection process
- Round 1: Technical interview (coding & conceptual questions)
- Round 2: Technical + coding challenge (Databricks & SQL)
- Round 3: HR interview
Prep kit
20 resourcesStudy material 4
Aptitude and Logical Reasoning Practice
Helps sharpen analytical skills needed for technical interviews at AXA XL.
Technical Interview Preparation
Provides coding challenges and interview questions relevant to data engineering roles.
Interview Strategy Guides
Offers insights into interview processes and common questions for tech roles.
Coding Problem Sets
Practises algorithmic challenges useful for coding rounds at AXA XL.
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
Axa Interview Preparation Corner
Comprehensive guide covering typical interview formats, common questions, and preparation tips for Axa 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, Databricks, Pyspark, Delta Lake, Azure Data Platform, Git, Devops) & 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
Axa 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 Axa as a Data Engineer?
Highlight Axa's market reputation, recent tech innovations, and how your skills in Python directly solve their team's objectives.
Highlight Axa'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.
8-12 LPA
Full-Time Job
Gurgaon, Haryana, India
Dec 01, 2026
