AWS Data Engineer
Capgemini
AWS Data Engineer
Job Description
AWS Data Engineer
📋 Job Overview
The AWS Data Engineer role at Capgemini Invent involves designing and implementing scalable data pipelines on AWS to support advanced analytics and ML solutions. The position requires collaboration with data teams to ensure data quality, governance, and security while optimizing ETL processes and driving automation. With 8-14 years of experience, the role focuses on building resilient big data ecosystems using AWS services and related technologies.
📍 Location: Bangalore
💼 Experience Level: Experienced Professionals
🏢 Business Unit: INVENT
🎯 Key Responsibilities
- Design and implement scalable, secure, and high-performance data pipelines on AWS Cloud
- Collaborate with data scientists, analysts, and application teams to deliver advanced analytics and ML-ready data solutions
- Ensure data quality, governance, and security across all ingestion, transformation, and storage layers
- Optimize ETL workflows, SQL queries, and big data processing for efficiency and reliability
- Drive automation and CI/CD practices for seamless deployment and maintenance of data solutions
- Monitor and troubleshoot data pipeline performance, ensuring accuracy and resilience
✅ Required Qualifications
- 8–14 years of experience in AWS-based data engineering and big data ecosystems
- Hands-on expertise in AWS services: Kinesis, EMR, Glue, RDS, Athena, RedShift, Lambda, and S3
- Strong programming skills in Python and PySpark, with advanced SQL optimization capabilities
- Proven experience with big data tools (Hadoop, Spark, Kafka) and data lake/data warehouse architectures
- Familiarity with relational (SQL Server, MySQL, PostgreSQL, Oracle) and NoSQL databases (Cassandra, MongoDB)
🛠️ Required Skills
- AWS services: Kinesis, EMR, Glue, RDS, Athena, RedShift, Lambda, S3
- Programming: Python, PySpark
- SQL optimization
- Big data tools: Hadoop, Spark, Kafka
- Data architectures: data lake, data warehouse
- Relational databases: SQL Server, MySQL, PostgreSQL, Oracle
- NoSQL databases: Cassandra, MongoDB
- ETL workflows
- CI/CD practices
- Data governance and security
Locations
- Bangalore, 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
- AWS services: Kinesis, EMR, Glue, RDS, Athena, RedShift, Lambda, S3intermediate
- Programming: Python, PySparkintermediate
- SQL optimizationintermediate
- Big data tools: Hadoop, Spark, Kafkaintermediate
- Data architectures: data lake, data warehouseintermediate
- Relational databases: SQL Server, MySQL, PostgreSQL, Oracleintermediate
- NoSQL databases: Cassandra, MongoDBintermediate
- ETL workflowsintermediate
- CI/CD practicesintermediate
- Data governance and securityintermediate
Required Qualifications
- 8–14 years of experience in AWS-based data engineering and big data ecosystems (experience)
- Hands-on expertise in AWS services: Kinesis, EMR, Glue, RDS, Athena, RedShift, Lambda, and S3 (experience)
- Strong programming skills in Python and PySpark, with advanced SQL optimization capabilities (experience)
- Proven experience with big data tools (Hadoop, Spark, Kafka) and data lake/data warehouse architectures (experience)
- Familiarity with relational (SQL Server, MySQL, PostgreSQL, Oracle) and NoSQL databases (Cassandra, MongoDB) (experience)
Responsibilities
- Design and implement scalable, secure, and high-performance data pipelines on AWS Cloud
- Collaborate with data scientists, analysts, and application teams to deliver advanced analytics and ML-ready data solutions
- Ensure data quality, governance, and security across all ingestion, transformation, and storage layers
- Optimize ETL workflows, SQL queries, and big data processing for efficiency and reliability
- Drive automation and CI/CD practices for seamless deployment and maintenance of data solutions
- Monitor and troubleshoot data pipeline performance, ensuring accuracy and resilience
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Answer 10 quick questions to check your fit for AWS Data Engineer @ Capgemini.
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AWS Data Engineer
Capgemini
AWS Data Engineer
Job Description
AWS Data Engineer
📋 Job Overview
The AWS Data Engineer role at Capgemini Invent involves designing and implementing scalable data pipelines on AWS to support advanced analytics and ML solutions. The position requires collaboration with data teams to ensure data quality, governance, and security while optimizing ETL processes and driving automation. With 8-14 years of experience, the role focuses on building resilient big data ecosystems using AWS services and related technologies.
📍 Location: Bangalore
💼 Experience Level: Experienced Professionals
🏢 Business Unit: INVENT
🎯 Key Responsibilities
- Design and implement scalable, secure, and high-performance data pipelines on AWS Cloud
- Collaborate with data scientists, analysts, and application teams to deliver advanced analytics and ML-ready data solutions
- Ensure data quality, governance, and security across all ingestion, transformation, and storage layers
- Optimize ETL workflows, SQL queries, and big data processing for efficiency and reliability
- Drive automation and CI/CD practices for seamless deployment and maintenance of data solutions
- Monitor and troubleshoot data pipeline performance, ensuring accuracy and resilience
✅ Required Qualifications
- 8–14 years of experience in AWS-based data engineering and big data ecosystems
- Hands-on expertise in AWS services: Kinesis, EMR, Glue, RDS, Athena, RedShift, Lambda, and S3
- Strong programming skills in Python and PySpark, with advanced SQL optimization capabilities
- Proven experience with big data tools (Hadoop, Spark, Kafka) and data lake/data warehouse architectures
- Familiarity with relational (SQL Server, MySQL, PostgreSQL, Oracle) and NoSQL databases (Cassandra, MongoDB)
🛠️ Required Skills
- AWS services: Kinesis, EMR, Glue, RDS, Athena, RedShift, Lambda, S3
- Programming: Python, PySpark
- SQL optimization
- Big data tools: Hadoop, Spark, Kafka
- Data architectures: data lake, data warehouse
- Relational databases: SQL Server, MySQL, PostgreSQL, Oracle
- NoSQL databases: Cassandra, MongoDB
- ETL workflows
- CI/CD practices
- Data governance and security
Locations
- Bangalore, 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
- AWS services: Kinesis, EMR, Glue, RDS, Athena, RedShift, Lambda, S3intermediate
- Programming: Python, PySparkintermediate
- SQL optimizationintermediate
- Big data tools: Hadoop, Spark, Kafkaintermediate
- Data architectures: data lake, data warehouseintermediate
- Relational databases: SQL Server, MySQL, PostgreSQL, Oracleintermediate
- NoSQL databases: Cassandra, MongoDBintermediate
- ETL workflowsintermediate
- CI/CD practicesintermediate
- Data governance and securityintermediate
Required Qualifications
- 8–14 years of experience in AWS-based data engineering and big data ecosystems (experience)
- Hands-on expertise in AWS services: Kinesis, EMR, Glue, RDS, Athena, RedShift, Lambda, and S3 (experience)
- Strong programming skills in Python and PySpark, with advanced SQL optimization capabilities (experience)
- Proven experience with big data tools (Hadoop, Spark, Kafka) and data lake/data warehouse architectures (experience)
- Familiarity with relational (SQL Server, MySQL, PostgreSQL, Oracle) and NoSQL databases (Cassandra, MongoDB) (experience)
Responsibilities
- Design and implement scalable, secure, and high-performance data pipelines on AWS Cloud
- Collaborate with data scientists, analysts, and application teams to deliver advanced analytics and ML-ready data solutions
- Ensure data quality, governance, and security across all ingestion, transformation, and storage layers
- Optimize ETL workflows, SQL queries, and big data processing for efficiency and reliability
- Drive automation and CI/CD practices for seamless deployment and maintenance of data solutions
- Monitor and troubleshoot data pipeline performance, ensuring accuracy and resilience
Tags & Categories
Answer 10 quick questions to check your fit for AWS Data Engineer @ Capgemini.
10 Questions
~2 Minutes
Instant Score
Related Books and Jobs