Capgemini – Data Analytics Engineer

Job Description

Capgemini is seeking a skilled Data Analytics Engineer to join its data and analytics team across Pune, Mumbai, and Bangalore. In this role, you will work with large and complex datasets to extract meaningful insights that help organizations make informed business decisions. As part of Capgemini’s transformation consulting practice, you will collaborate with cross-functional teams to design data-driven solutions that improve operational efficiency, enhance customer experiences, and support digital transformation initiatives. The position offers an opportunity to work with advanced analytics technologies, cloud platforms, and modern data engineering frameworks.

The role involves analyzing structured and unstructured datasets using tools such as SQL, Python, and PySpark while developing scalable data models and analytics datasets. You will design and build interactive dashboards using business intelligence tools like Power BI or Tableau to help business teams visualize and interpret data effectively. By working closely with data engineers and cloud specialists, you will contribute to building data pipelines, ensuring data quality, and optimizing performance of analytics platforms deployed on cloud environments such as Azure, AWS, or Google Cloud Platform.

Additionally, the Data Analytics Engineer will support AI-driven analytics initiatives by performing exploratory data analysis, preparing features for machine learning models, and ensuring accurate data transformation processes. The role requires working in agile development environments where collaboration, innovation, and continuous improvement are encouraged. By combining strong analytical skills with technical expertise, you will contribute to building modern data ecosystems that help organizations unlock the full potential of their data.

Roles & Responsibilities

• Analyze large datasets to identify patterns, trends, and insights that support strategic business decisions and operational improvements.

• Develop and optimize SQL queries to retrieve, transform, and analyze data from various data sources efficiently.

• Build interactive dashboards and reports using business intelligence tools such as Power BI, Tableau, or Looker to visualize key performance metrics.

• Perform data preparation, cleansing, and transformation using programming languages such as Python and frameworks like PySpark.

• Work with big data processing platforms such as Databricks to analyze large-scale datasets and support advanced analytics workflows.

• Collaborate with data engineering teams to develop and maintain data pipelines that enable reliable and scalable data processing.

• Apply data modelling techniques including dimensional modelling and fact/dimension table design to create reusable analytical datasets.

• Ensure data quality by implementing validation checks, monitoring data pipelines, and identifying inconsistencies in datasets.

• Support AI and machine learning initiatives by performing exploratory data analysis and preparing data features for predictive models.

• Participate in agile development activities including sprint planning, backlog refinement, and regular team demonstrations.

• Maintain technical documentation, data governance guidelines, and project tracking information using tools such as JIRA and Confluence.

Requirements & Eligibility

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

• Strong proficiency in SQL for data analysis, querying databases, and validating data integrity.

• Hands-on experience with Python for data analysis, automation, and data transformation tasks.

• Experience with business intelligence tools such as Power BI, Tableau, Looker, or similar analytics platforms.

• Familiarity with cloud platforms such as Microsoft Azure, Amazon Web Services (AWS), or Google Cloud Platform (GCP).

• Practical experience working with PySpark and Databricks for large-scale data processing and distributed analytics.

• Solid understanding of data modelling concepts including dimensional modelling, fact tables, and dimension tables.

• Experience collaborating with data engineering teams on data pipelines, data processing workflows, and performance optimization.

• Familiarity with agile methodologies and project management tools such as JIRA, Confluence, and DevOps platforms.

• Strong analytical thinking and problem-solving skills with the ability to derive insights from complex datasets.

• Excellent communication and teamwork skills to collaborate effectively with business stakeholders and technical teams.

Expected Salary

The average salary for a Data Analytics Engineer at large consulting and technology firms in India typically ranges between ₹8 LPA to ₹18 LPA, depending on experience, expertise in SQL and Python, and familiarity with cloud data platforms such as Azure, AWS, or GCP. Professionals with strong experience in big data tools like PySpark and Databricks may receive higher compensation along with performance bonuses and career growth opportunities.

🚨 Stop Scrolling – This Could Be Your Shortcut to Interviews

Most candidates apply to 100+ jobs and never hear back.
The real reason? They don’t know where recruiters are actually hiring from.

Our March Hiring PDF includes verified HR emails and hiring details from companies like:

Dentsu, IBM, HCL, PwC, LTIMindtree, Wipro, Cognizant, Deloitte, Capgemini, Amazon, TCS, Infosys, EPAM, EY, NTT Data, Tech Mahindra, Fractal, GlobalLogic, Coforge, UST and many more.

Inside you’ll find:
✔ 200+ Fresher Job Opportunities
✔ 2500+ Verified HR Emails & Contacts
✔ Direct Hiring + Consultancy Openings
✔ IT & Non-IT Roles

🔥 60+ students placed recently using these hiring leads

👉 Grab the March Hiring List Now: March Hiring PDF