Data Engineer
Capgemini
Data Engineer
Job Description
Data Engineer
📋 Job Overview
As a Senior Azure Data Engineer at Capgemini Invent, you will architect, develop, and optimize enterprise-grade data solutions on Microsoft Azure, leading the design of scalable data platforms, lakes, and analytics systems aligned with business goals. The role requires deep expertise in Azure and AWS services, data modeling, pipeline orchestration, and mentoring junior engineers while collaborating across teams. A foundational understanding of Generative AI for integrating AI-driven enhancements into workflows is desirable.
📍 Location: Mumbai
💼 Experience Level: Experienced Professionals
🏢 Business Unit: INVENT
🎯 Key Responsibilities
- Architect and implement end-to-end data solutions using ADF, Synapse Analytics, Databricks, AWS Glue, AWS Data Pipeline, Amazon EMR
- Design and manage data lakes, data warehouses, and streaming data architectures
- Lead cloud migration and modernization initiatives for legacy data systems
- Ensure data governance, security, and compliance across all Azure data assets
- Collaborate with enterprise architects, data scientists, and business stakeholders to deliver scalable analytics solutions
- Implement CI/CD pipelines, DevOps practices, and Infrastructure as Code (IaC) using tools like Terraform or ARM templates
- Evaluate and integrate GenAI capabilities (e.g., Azure OpenAI, Copilot Studio, Amazon SageMaker, Bedrock, Kendra) into data engineering workflows
- Guide architectural decisions and mentor junior engineers on best practices
✅ Required Qualifications
- Bachelor’s/master’s degree in computer science, Engineering, or related field
- 9–12 years of experience in data engineering or architecture roles
- At least 4+ years on Azure/AWS
⭐ Preferred Qualifications
- Familiarity with GenAI tools like Azure OpenAI, AWS AI Services, Hugging Face, or LangChain
- Knowledge of streaming data platforms such as Kafka, Azure Event Hubs, Azure Stream Analytics, Amazon Kinesis
🛠️ Required Skills
- Azure Data Factory
- Azure Synapse
- Azure Databricks
- Azure SQL
- Cosmos DB
- Blob Storage
- Data Lake Storage Gen2
- AWS Glue
- Redshift
- EMR
- DynamoDB
- RDS
- S3
- ETL/ELT pipelines
- Data modelling
- MPP databases (Snowflake, Redshift, Teradata)
- BI Reporting (Power BI)
- Python
- SQL
- Spark
- Cloud migration and solution architecture for large-scale data systems
- Excellent communication, leadership, and stakeholder management skills
🎁 Benefits & Perks
- Flexible work arrangements including remote work and flexible hours for work-life balance
- Career growth programs and diverse professional opportunities
- Valuable certifications in latest technologies such as Generative AI
Locations
- Mumbai, India
Salary
Estimated Salary Rangemedium confidence
2,500,000 - 4,200,000 INR / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- Azure Data Factoryintermediate
- Azure Synapseintermediate
- Azure Databricksintermediate
- Azure SQLintermediate
- Cosmos DBintermediate
- Blob Storageintermediate
- Data Lake Storage Gen2intermediate
- AWS Glueintermediate
- Redshiftintermediate
- EMRintermediate
- DynamoDBintermediate
- RDSintermediate
- S3intermediate
- ETL/ELT pipelinesintermediate
- Data modellingintermediate
- MPP databases (Snowflake, Redshift, Teradata)intermediate
- BI Reporting (Power BI)intermediate
- Pythonintermediate
- SQLintermediate
- Sparkintermediate
- Cloud migration and solution architecture for large-scale data systemsintermediate
- Excellent communication, leadership, and stakeholder management skillsintermediate
Required Qualifications
- Bachelor’s/master’s degree in computer science, Engineering, or related field (experience)
- 9–12 years of experience in data engineering or architecture roles (experience)
- At least 4+ years on Azure/AWS (experience)
Preferred Qualifications
- Familiarity with GenAI tools like Azure OpenAI, AWS AI Services, Hugging Face, or LangChain (experience)
- Knowledge of streaming data platforms such as Kafka, Azure Event Hubs, Azure Stream Analytics, Amazon Kinesis (experience)
Responsibilities
- Architect and implement end-to-end data solutions using ADF, Synapse Analytics, Databricks, AWS Glue, AWS Data Pipeline, Amazon EMR
- Design and manage data lakes, data warehouses, and streaming data architectures
- Lead cloud migration and modernization initiatives for legacy data systems
- Ensure data governance, security, and compliance across all Azure data assets
- Collaborate with enterprise architects, data scientists, and business stakeholders to deliver scalable analytics solutions
- Implement CI/CD pipelines, DevOps practices, and Infrastructure as Code (IaC) using tools like Terraform or ARM templates
- Evaluate and integrate GenAI capabilities (e.g., Azure OpenAI, Copilot Studio, Amazon SageMaker, Bedrock, Kendra) into data engineering workflows
- Guide architectural decisions and mentor junior engineers on best practices
Benefits
- general: Flexible work arrangements including remote work and flexible hours for work-life balance
- general: Career growth programs and diverse professional opportunities
- general: Valuable certifications in latest technologies such as Generative AI
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Data Engineer
Capgemini
Data Engineer
Job Description
Data Engineer
📋 Job Overview
As a Senior Azure Data Engineer at Capgemini Invent, you will architect, develop, and optimize enterprise-grade data solutions on Microsoft Azure, leading the design of scalable data platforms, lakes, and analytics systems aligned with business goals. The role requires deep expertise in Azure and AWS services, data modeling, pipeline orchestration, and mentoring junior engineers while collaborating across teams. A foundational understanding of Generative AI for integrating AI-driven enhancements into workflows is desirable.
📍 Location: Mumbai
💼 Experience Level: Experienced Professionals
🏢 Business Unit: INVENT
🎯 Key Responsibilities
- Architect and implement end-to-end data solutions using ADF, Synapse Analytics, Databricks, AWS Glue, AWS Data Pipeline, Amazon EMR
- Design and manage data lakes, data warehouses, and streaming data architectures
- Lead cloud migration and modernization initiatives for legacy data systems
- Ensure data governance, security, and compliance across all Azure data assets
- Collaborate with enterprise architects, data scientists, and business stakeholders to deliver scalable analytics solutions
- Implement CI/CD pipelines, DevOps practices, and Infrastructure as Code (IaC) using tools like Terraform or ARM templates
- Evaluate and integrate GenAI capabilities (e.g., Azure OpenAI, Copilot Studio, Amazon SageMaker, Bedrock, Kendra) into data engineering workflows
- Guide architectural decisions and mentor junior engineers on best practices
✅ Required Qualifications
- Bachelor’s/master’s degree in computer science, Engineering, or related field
- 9–12 years of experience in data engineering or architecture roles
- At least 4+ years on Azure/AWS
⭐ Preferred Qualifications
- Familiarity with GenAI tools like Azure OpenAI, AWS AI Services, Hugging Face, or LangChain
- Knowledge of streaming data platforms such as Kafka, Azure Event Hubs, Azure Stream Analytics, Amazon Kinesis
🛠️ Required Skills
- Azure Data Factory
- Azure Synapse
- Azure Databricks
- Azure SQL
- Cosmos DB
- Blob Storage
- Data Lake Storage Gen2
- AWS Glue
- Redshift
- EMR
- DynamoDB
- RDS
- S3
- ETL/ELT pipelines
- Data modelling
- MPP databases (Snowflake, Redshift, Teradata)
- BI Reporting (Power BI)
- Python
- SQL
- Spark
- Cloud migration and solution architecture for large-scale data systems
- Excellent communication, leadership, and stakeholder management skills
🎁 Benefits & Perks
- Flexible work arrangements including remote work and flexible hours for work-life balance
- Career growth programs and diverse professional opportunities
- Valuable certifications in latest technologies such as Generative AI
Locations
- Mumbai, India
Salary
Estimated Salary Rangemedium confidence
2,500,000 - 4,200,000 INR / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- Azure Data Factoryintermediate
- Azure Synapseintermediate
- Azure Databricksintermediate
- Azure SQLintermediate
- Cosmos DBintermediate
- Blob Storageintermediate
- Data Lake Storage Gen2intermediate
- AWS Glueintermediate
- Redshiftintermediate
- EMRintermediate
- DynamoDBintermediate
- RDSintermediate
- S3intermediate
- ETL/ELT pipelinesintermediate
- Data modellingintermediate
- MPP databases (Snowflake, Redshift, Teradata)intermediate
- BI Reporting (Power BI)intermediate
- Pythonintermediate
- SQLintermediate
- Sparkintermediate
- Cloud migration and solution architecture for large-scale data systemsintermediate
- Excellent communication, leadership, and stakeholder management skillsintermediate
Required Qualifications
- Bachelor’s/master’s degree in computer science, Engineering, or related field (experience)
- 9–12 years of experience in data engineering or architecture roles (experience)
- At least 4+ years on Azure/AWS (experience)
Preferred Qualifications
- Familiarity with GenAI tools like Azure OpenAI, AWS AI Services, Hugging Face, or LangChain (experience)
- Knowledge of streaming data platforms such as Kafka, Azure Event Hubs, Azure Stream Analytics, Amazon Kinesis (experience)
Responsibilities
- Architect and implement end-to-end data solutions using ADF, Synapse Analytics, Databricks, AWS Glue, AWS Data Pipeline, Amazon EMR
- Design and manage data lakes, data warehouses, and streaming data architectures
- Lead cloud migration and modernization initiatives for legacy data systems
- Ensure data governance, security, and compliance across all Azure data assets
- Collaborate with enterprise architects, data scientists, and business stakeholders to deliver scalable analytics solutions
- Implement CI/CD pipelines, DevOps practices, and Infrastructure as Code (IaC) using tools like Terraform or ARM templates
- Evaluate and integrate GenAI capabilities (e.g., Azure OpenAI, Copilot Studio, Amazon SageMaker, Bedrock, Kendra) into data engineering workflows
- Guide architectural decisions and mentor junior engineers on best practices
Benefits
- general: Flexible work arrangements including remote work and flexible hours for work-life balance
- general: Career growth programs and diverse professional opportunities
- general: Valuable certifications in latest technologies such as Generative AI
Tags & Categories
Answer 10 quick questions to check your fit for Data Engineer @ Capgemini.
10 Questions
~2 Minutes
Instant Score
Related Books and Jobs