Sr. Data Science Engineer
Capgemini
Sr. Data Science Engineer
Job Description
Sr. Data Science Engineer
📋 Job Overview
The Sr. Data Science Engineer role at Capgemini involves designing and maintaining scalable data pipelines, integrating diverse data sources, and collaborating with data scientists to deploy ML models in production. Responsibilities include implementing CI/CD for ML workflows, ensuring data quality and security, and mentoring junior engineers while staying current with big data, cloud, and AI technologies. This position offers opportunities to shape your career in a global, innovative environment focused on technology transformation.
📍 Location: Bangalore
💼 Experience Level: Experienced Professionals
🏢 Business Unit: Other Delivery units
🎯 Key Responsibilities
- Design and maintain scalable data pipelines and architectures
- Integrate structured, semi-structured, and unstructured data from multiple sources
- Collaborate with data scientists to deploy and optimize ML models in production
- Implement CI/CD pipelines for machine learning workflows
- Ensure data quality, integrity, and compliance with security standards
- Develop automated data validation and cleansing processes
- Mentor junior engineers and promote best practices in data engineering and ML Ops
- Stay updated on big data, cloud, and AI technologies
✅ Required Qualifications
- 5+ years in data engineering or data science roles
- Strong programming in Python, SQL, and experience with Spark/Hadoop
- Proficiency in cloud platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes)
- Experience with ETL tools, data warehousing, and stream processing
- Familiarity with ML frameworks (TensorFlow, PyTorch, Scikit-learn)
- Understanding of model lifecycle management and ML Ops
🛠️ Required Skills
- Python
- SQL
- Spark
- Hadoop
- AWS
- Azure
- GCP
- Docker
- Kubernetes
- ETL tools
- Data warehousing
- Stream processing
- TensorFlow
- PyTorch
- Scikit-learn
- Model lifecycle management
- ML Ops
- CI/CD pipelines
- Data validation and cleansing
- Big data technologies
- Cloud technologies
- AI technologies
🎁 Benefits & Perks
- Range of career paths and internal opportunities within Capgemini group
- Personalized career guidance from leaders
- Comprehensive wellness benefits including health checks, telemedicine, insurance with top-ups, elder care, partner coverage, and new parent support via flexible work
- Access to industry's largest digital learning platform with 250,000+ courses and numerous certifications
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
- Pythonintermediate
- SQLintermediate
- Sparkintermediate
- Hadoopintermediate
- AWSintermediate
- Azureintermediate
- GCPintermediate
- Dockerintermediate
- Kubernetesintermediate
- ETL toolsintermediate
- Data warehousingintermediate
- Stream processingintermediate
- TensorFlowintermediate
- PyTorchintermediate
- Scikit-learnintermediate
- Model lifecycle managementintermediate
- ML Opsintermediate
- CI/CD pipelinesintermediate
- Data validation and cleansingintermediate
- Big data technologiesintermediate
- Cloud technologiesintermediate
- AI technologiesintermediate
Required Qualifications
- 5+ years in data engineering or data science roles (experience)
- Strong programming in Python, SQL, and experience with Spark/Hadoop (experience)
- Proficiency in cloud platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes) (experience)
- Experience with ETL tools, data warehousing, and stream processing (experience)
- Familiarity with ML frameworks (TensorFlow, PyTorch, Scikit-learn) (experience)
- Understanding of model lifecycle management and ML Ops (experience)
Responsibilities
- Design and maintain scalable data pipelines and architectures
- Integrate structured, semi-structured, and unstructured data from multiple sources
- Collaborate with data scientists to deploy and optimize ML models in production
- Implement CI/CD pipelines for machine learning workflows
- Ensure data quality, integrity, and compliance with security standards
- Develop automated data validation and cleansing processes
- Mentor junior engineers and promote best practices in data engineering and ML Ops
- Stay updated on big data, cloud, and AI technologies
Benefits
- general: Range of career paths and internal opportunities within Capgemini group
- general: Personalized career guidance from leaders
- general: Comprehensive wellness benefits including health checks, telemedicine, insurance with top-ups, elder care, partner coverage, and new parent support via flexible work
- general: Access to industry's largest digital learning platform with 250,000+ courses and numerous certifications
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Sr. Data Science Engineer
Capgemini
Sr. Data Science Engineer
Job Description
Sr. Data Science Engineer
📋 Job Overview
The Sr. Data Science Engineer role at Capgemini involves designing and maintaining scalable data pipelines, integrating diverse data sources, and collaborating with data scientists to deploy ML models in production. Responsibilities include implementing CI/CD for ML workflows, ensuring data quality and security, and mentoring junior engineers while staying current with big data, cloud, and AI technologies. This position offers opportunities to shape your career in a global, innovative environment focused on technology transformation.
📍 Location: Bangalore
💼 Experience Level: Experienced Professionals
🏢 Business Unit: Other Delivery units
🎯 Key Responsibilities
- Design and maintain scalable data pipelines and architectures
- Integrate structured, semi-structured, and unstructured data from multiple sources
- Collaborate with data scientists to deploy and optimize ML models in production
- Implement CI/CD pipelines for machine learning workflows
- Ensure data quality, integrity, and compliance with security standards
- Develop automated data validation and cleansing processes
- Mentor junior engineers and promote best practices in data engineering and ML Ops
- Stay updated on big data, cloud, and AI technologies
✅ Required Qualifications
- 5+ years in data engineering or data science roles
- Strong programming in Python, SQL, and experience with Spark/Hadoop
- Proficiency in cloud platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes)
- Experience with ETL tools, data warehousing, and stream processing
- Familiarity with ML frameworks (TensorFlow, PyTorch, Scikit-learn)
- Understanding of model lifecycle management and ML Ops
🛠️ Required Skills
- Python
- SQL
- Spark
- Hadoop
- AWS
- Azure
- GCP
- Docker
- Kubernetes
- ETL tools
- Data warehousing
- Stream processing
- TensorFlow
- PyTorch
- Scikit-learn
- Model lifecycle management
- ML Ops
- CI/CD pipelines
- Data validation and cleansing
- Big data technologies
- Cloud technologies
- AI technologies
🎁 Benefits & Perks
- Range of career paths and internal opportunities within Capgemini group
- Personalized career guidance from leaders
- Comprehensive wellness benefits including health checks, telemedicine, insurance with top-ups, elder care, partner coverage, and new parent support via flexible work
- Access to industry's largest digital learning platform with 250,000+ courses and numerous certifications
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
- Pythonintermediate
- SQLintermediate
- Sparkintermediate
- Hadoopintermediate
- AWSintermediate
- Azureintermediate
- GCPintermediate
- Dockerintermediate
- Kubernetesintermediate
- ETL toolsintermediate
- Data warehousingintermediate
- Stream processingintermediate
- TensorFlowintermediate
- PyTorchintermediate
- Scikit-learnintermediate
- Model lifecycle managementintermediate
- ML Opsintermediate
- CI/CD pipelinesintermediate
- Data validation and cleansingintermediate
- Big data technologiesintermediate
- Cloud technologiesintermediate
- AI technologiesintermediate
Required Qualifications
- 5+ years in data engineering or data science roles (experience)
- Strong programming in Python, SQL, and experience with Spark/Hadoop (experience)
- Proficiency in cloud platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes) (experience)
- Experience with ETL tools, data warehousing, and stream processing (experience)
- Familiarity with ML frameworks (TensorFlow, PyTorch, Scikit-learn) (experience)
- Understanding of model lifecycle management and ML Ops (experience)
Responsibilities
- Design and maintain scalable data pipelines and architectures
- Integrate structured, semi-structured, and unstructured data from multiple sources
- Collaborate with data scientists to deploy and optimize ML models in production
- Implement CI/CD pipelines for machine learning workflows
- Ensure data quality, integrity, and compliance with security standards
- Develop automated data validation and cleansing processes
- Mentor junior engineers and promote best practices in data engineering and ML Ops
- Stay updated on big data, cloud, and AI technologies
Benefits
- general: Range of career paths and internal opportunities within Capgemini group
- general: Personalized career guidance from leaders
- general: Comprehensive wellness benefits including health checks, telemedicine, insurance with top-ups, elder care, partner coverage, and new parent support via flexible work
- general: Access to industry's largest digital learning platform with 250,000+ courses and numerous certifications
Tags & Categories
Answer 10 quick questions to check your fit for Sr. Data Science Engineer @ Capgemini.
10 Questions
~2 Minutes
Instant Score
Related Books and Jobs