N #3869
Applications open
Full-Time Job
Azure Data Engineer
Capgemini
About the role
About the Role
We are looking for an experienced and passionate Azure Data Engineer to join our team at Capgemini. You will work with leading global brands to design, implement, and optimize modern data solutions using Microsoft Azure cloud services.
Key Responsibilities
- Design, develop, and maintain end-to-end data ingestion, transformation, and processing pipelines using Azure Data Factory (ADF) and Azure Databricks (ADB).
- Develop scalable ETL/ELT solutions to integrate data from multiple sources into Azure data platforms.
- Build and optimize data processing frameworks using PySpark, Python, Scala, and Hive within Azure Databricks environments.
- Implement and manage enterprise-scale data warehousing solutions using Azure SQL Database, Azure SQL Data Warehouse, and Azure Synapse Analytics.
- Collaborate with business analysts, architects, and stakeholders to gather requirements and translate them into technical solutions.
- Ensure data quality, data governance, security, and compliance standards are maintained across all data assets.
- Perform performance tuning, query optimization, and troubleshooting of large-scale data processing workloads.
- Design, build, and support distributed data workflows and real-time/batch processing solutions.
- Develop and maintain CI/CD pipelines for data engineering applications using Azure DevOps and related tools.
- Support cloud migration initiatives and modernization of legacy data platforms to Azure.
- Create technical documentation, deployment procedures, and operational support guides.
- Participate in code reviews and follow industry best practices for software engineering and data architecture.
Required Skills
& Experience Primary Skills (Mandatory) Candidates must have strong hands-on experience in at least one of the following: Azure Data Factory (ADF) Azure Databricks (ADB)
Core
Technical Skills
Strong experience with Azure Databricks and Azure Data Factory Expertise in designing ETL/ELT pipelines and data integration solutions Hands-on experience with: Azure SQL Database Azure SQL Data Warehouse Azure Synapse Analytics Proficiency in: Python PySpark Scala Hive Experience working with large-scale structured and unstructured datasets Strong understanding of data warehousing concepts and dimensional modeling Experience in CI/CD implementation and deployment automation in Azure environments Knowledge of Azure DevOps, Git, and version control practices Understanding of cloud-native architecture, security, and monitoring best practices
Preferred Skills
Experience with Delta Lake and Spark optimization techniques Knowledge of Azure Data Lake Storage (ADLS Gen2) Exposure to real-time data processing and streaming solutions Azure certifications related to Data Engineering or Cloud Technologies Experience working in Agile/Scrum environments
Eligibility & process
Who can apply
Experience: 6-11 Yrs. Open to matching qualifications.
Skills & stack
Selection process
- Online Assessment / Technical Interview Rounds / HR Review
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 Capgemini 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
Capgemini Interview Preparation Corner
Comprehensive guide covering typical interview formats, common questions, and preparation tips for Capgemini 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 (Pyspark, Azure Databricks, Azure Data Factory, Microsoft Fabric, SQL, Microsoft, Microsoft Azure, Factory) & 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
Capgemini 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 Capgemini as a Azure Data Engineer?
Highlight Capgemini's market reputation, recent tech innovations, and how your skills in Pyspark directly solve their team's objectives.
Highlight Capgemini's market reputation, recent tech innovations, and how your skills in Pyspark 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 (Best in Industry)
Bengaluru, India
Experience: 6-11 Yrs. Open to matching quโฆ
