Explore Exciting Fresher & Experienced Roles at Eaton

Eaton is hiring an Apprentice IT for the Pune location. Please read the complete information carefully and apply if you are eligible.

Eligibility Criteria :

• Qualification: Bachelor’s degree in Engineering (B.E./B.Tech) in Artificial Intelligence, Machine Learning, Data Science, Computer Science (with strong AI/ML coursework)

• Experience:  0 - 2 Years
  

• Skills: Python, SQL

Responsibilities:

• Assist in building and training machine learning models for structured and unstructured data use cases
• Perform data analysis, preprocessing, and feature engineering on large datasets
• Support experimentation using AutoML and custom ML approaches
• Evaluate model performance and assist in tuning for accuracy and robustness
• Work with AI/ML platforms and tools for model development and experimentation
• Collaborate with engineers and analysts to understand business problems and translate them into ML tasks
• Document experiments, learnings, and model outcomes clearly
• Follow best practices for responsible AI, data governance, and security

Required Skills:

Strong fundamentals in:
o    Machine Learning algorithms
o    Statistics and linear algebra
o    Data structures and basic algorithms
Working knowledge of Python
Familiarity with ML libraries such as:
o    scikit learn
o    TensorFlow or PyTorch (basic exposure is sufficient)
•    Basic understanding of SQL and working with datasets

Stipend: ₹4LPA—₹8 LPA (Glassdoor)

Apply here

Eaton is hiring an Data Engineering Apprentice for the Pune location. Please read the complete information carefully and apply if you are eligible.

Eligibility Criteria :

• Qualification: Undergraduate in Computer Science, Information Technology, Data Science, Engineering, or a related field

• Experience:  0 - 2 Years
  

• Skills: Python, SQL

Responsibilities:

Data Engineering & Platform Support
•    Assist in building and maintaining data pipelines for ingesting, transforming, and validating data from various source systems.
•    Support data transformations using SQL and Python under established engineering standards.
•    Help with data quality checks, reconciliation processes, and basic troubleshooting of data issues.
•    Participate in documenting data pipelines, table definitions, and engineering artifacts.
Learning & Engineering Practices
•    Learn and apply modern data engineering practices including ELT/ETL pipelines, version control, and CI/CD fundamentals.
•    Follow enterprise data engineering standards for naming conventions, data modeling, and code quality as defined by the team.
•    Gain hands-on exposure to cloud data platforms such as Snowflake and Azure-based data services.
•    Participate in code reviews and technical walkthroughs as a learning opportunity.
Collaboration & Communication
•    Work closely with senior data engineers, analysts, and product owners to understand business and technical requirements.
•    Support team activities such as sprint planning, backlog grooming, and sprint reviews in an Agile delivery model.
•    Communicate progress, issues, and learnings clearly to mentors and team members.
Data Governance & Compliance
•    Learn and adhere to data governance, security, and access control standards.
•    Assist in implementing basic data validation, audit columns, and control checks required for enterprise and finance data.

Required Skills:

•  Fundamental knowledge of SQL (SELECT, JOINs, basic aggregations).
•  Basic programming knowledge in Python or a similar language.
• Understanding of relational databases and data concepts (tables, keys, data types).
• Familiarity with basic data engineering or analytics concepts is a plus.
• Strong willingness to learn and take feedback positively.
• Good analytical and problem-solving skills.
• Clear written and verbal communication skills.
• Ability to work collaboratively in a team environment.
• Exposure to cloud platforms (Azure, AWS, or GCP) in coursework or projects.
• Basic familiarity with Snowflake, Spark, or data integration tools (e.g., ADF) is an advantage but not mandatory.
• Academic or personal projects involving data pipelines or databases.
• Interest in finance or enterprise data domains.

Stipend: ₹4LPA—₹8 LPA (Glassdoor)

Apply here