Capgemini Invent – Data Analytics

Job Description

Capgemini Invent is seeking skilled Data Analytics professionals who are passionate about transforming complex data into actionable insights that drive strategic business decisions. As part of Capgemini’s consulting and digital transformation division, this role focuses on leveraging advanced analytics, data engineering practices, and business intelligence tools to support organizations in making data-driven decisions. Professionals in this role will work closely with clients across industries to analyze large datasets, uncover hidden patterns, and generate meaningful insights that help businesses improve efficiency, optimize processes, and gain a competitive advantage in the market.

The Data Analytics professional will be responsible for building scalable analytical solutions using technologies such as SQL, Python, PySpark, and cloud platforms including Azure, AWS, or Google Cloud Platform. The role involves developing and optimizing data models, preparing analytical datasets, and designing interactive dashboards that provide stakeholders with clear and actionable business intelligence. By combining strong technical expertise with analytical thinking, the consultant will help organizations understand complex datasets and translate them into strategic recommendations.

In addition to analytics and reporting responsibilities, the role also involves collaborating with data engineering teams to develop data pipelines, ensure high data quality standards, and optimize system performance. Professionals will work in Agile delivery environments, contributing to sprint planning, backlog refinement, and project demonstrations. With Capgemini’s strong focus on innovation and emerging technologies such as AI and Generative AI, this position offers an excellent opportunity for data professionals to enhance their skills in cloud analytics, advanced data modelling, and AI-driven insights while contributing to large-scale digital transformation projects.

Roles & Responsibilities

1. Analyze large and complex datasets to identify patterns, trends, and business insights that support strategic decision-making across different departments and stakeholders.

2. Develop, optimize, and maintain SQL queries for data extraction, validation, and reporting, ensuring accurate and efficient data analysis processes.

3. Design, build, and maintain interactive business intelligence dashboards using tools such as Power BI, Tableau, or Looker to provide actionable insights to business users.

4. Perform data preparation, cleansing, transformation, and validation using Python, PySpark, and Databricks to ensure high-quality analytical datasets.

5. Collaborate with data engineering teams to develop and maintain scalable data pipelines that support large-scale data processing and analytics.

6. Apply data modelling techniques such as dimensional modelling, fact tables, and dimension tables to create scalable and reusable analytical data structures.

7. Work with cloud platforms including Microsoft Azure, AWS, or Google Cloud to access, process, and analyze enterprise-scale datasets.

8. Support AI-driven analytics initiatives by performing exploratory data analysis and preparing datasets suitable for machine learning models.

9. Participate in Agile project environments by contributing to sprint planning, backlog refinement, project demonstrations, and collaborative development activities.

10. Maintain documentation, governance artifacts, and technical project documentation using tools such as JIRA, Confluence, and DevOps platforms.

Requirements & Eligibility

1. Bachelor’s or Master’s degree in Computer Science, Data Science, Information Technology, Statistics, or a related quantitative field.

2. Strong proficiency in SQL for querying large datasets, performing complex joins, aggregations, and data validation tasks.

3. Hands-on experience with Python for data analysis, automation, and data transformation tasks within analytics workflows.

4. Experience with business intelligence tools such as Power BI, Tableau, Looker, or similar platforms for creating data visualizations and dashboards.

5. Familiarity with cloud platforms such as Microsoft Azure, Amazon Web Services (AWS), or Google Cloud Platform for data storage and processing.

6. Practical experience with big data technologies such as PySpark and Databricks for handling large-scale data processing and analytics.

7. Strong understanding of data modelling concepts including dimensional modelling, star schema design, and building fact and dimension tables.

8. Knowledge of Agile methodologies and project management tools such as JIRA and Confluence used in collaborative development environments.

9. Understanding of DevOps practices and CI/CD pipelines for deploying and maintaining data solutions efficiently.

10. Strong analytical thinking, problem-solving ability, and communication skills to translate complex data insights into meaningful business recommendations.

Expected Salary

The average salary for Data Analytics professionals in India typically ranges between ₹6 LPA to ₹15 LPA, depending on experience, technical skills, and expertise in cloud analytics and big data technologies. Candidates with strong knowledge of Python, SQL, cloud platforms, and BI tools often receive higher compensation, along with additional benefits such as certifications, training programs, and career growth opportunities within global consulting firms like Capgemini.

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