Apprentice – IT Development Program (Master Data Management)
Eaton — Pune, Maharashtra
₹3–7 LPA (HireSetu estimate) · Entry level · Freshers eligible · Full-time
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Quick answer: An apprentice seat on Eaton's master data management team in Pune. The bachelor's degree is the only stated requirement; SQL, Snowflake, Python and GenAI awareness all sit in the basic-knowledge pile.
From Eaton's job posting
What you’ll do
We are looking for a highly motivated Apprentice to join our Master Data Management (MDM) team. This role provides an excellent opportunity to gain hands-on experience in modern data platforms, including Snowflake, Data Architecture, Medallion Architecture, and emerging Generative AI (GenAI) capabilities.
The candidate will work closely with senior data engineers and architects to support data engineering, data quality, and data governance initiatives while building strong foundational skills in enterprise data management.
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Key Responsibilities
- Support development and maintenance of MDM solutions on modern cloud platforms (Snowflake preferred)
- Assist in implementing Medallion Architecture (Bronze, Silver, Gold layers) for structured data processing
- Write and optimize SQL queries for data transformation, validation, and analysis
- Participate in data modeling and data architecture discussions
- Support data quality, cleansing, and standardization initiatives
- Assist in building data pipelines, streamlit apps and workflows
- Explore and prototype GenAI-driven use cases in data management (e.g., intelligent matching, enrichment, automation)
- Collaborate with cross-functional teams including data stewards, business stakeholders, and engineering teams
- Document technical designs, transformations, and best practices
Qualifications
- Bachelor’s degree in Computer Science, Information Technology, Data Engineering, or related field
Skills
- Basic understanding of SQL (joins, aggregations, transformations)
- Familiarity with Data Warehousing concepts
- Knowledge of Snowflake or any cloud data platform (preferred)
- Understanding of Data Architecture fundamentals
- Awareness of Medallion Architecture (Bronze-Silver-Gold layers)
- Basic knowledge or interest in Generative AI (GenAI) concepts and applications
- Exposure to ETL/ELT processes is a plus
- Certification or coursework in Snowflake, Data Engineering, or AI/ML
- Hands-on academic or project experience in data-related domains
Soft Skills & Behavioral Competencies
- Strong analytical and problem-solving mindset
- Good communication skills (ability to explain technical concepts clearly)
- Willingness to learn and adapt in a fast-paced environment
- Attention to detail and data accuracy
- Ability to work collaboratively in a team environment
- Proactive attitude with a self-starter mindset
- Openness to feedback and continuous improvement
- Strong time management and prioritization skills
- Exposure to tools like Python or data integration tools
- Understanding of data governance and data quality frameworks
- Basic knowledge of APIs and data services
Text from Eaton's official job posting, formatted by HireSetu. The employer's own page is the final word on details.
HireSetu's note
One hard filter, the rest is a head start
The degree is the gate: computer science, IT, data engineering or something adjacent, and it's the only line in the qualifications written as a requirement. Everything under the skills list says basic, familiarity, awareness, interest or exposure, and ETL/ELT is openly marked a plus. Snowflake gets the preferred tag twice, so a free Snowflake course or one small project built on a trial account separates you from most freshers applying at this level. Python, APIs and data governance sit in the same optional pile.
The GenAI bullet deserves a slow read. Explore and prototype means nobody expects shipped GenAI work, only a view on where matching or enrichment could use it and why that beats plain rule-based logic. Apprentice contracts in India run for a fixed term, so it's fair to establish the length and what typically follows it.
The unglamorous core of MDM
Master data management is mostly cleaning. Duplicate customer and vendor records, addresses written three different ways, the same entity entered by three systems that never agreed on a format. The work is deciding which version survives as the golden record and keeping it accurate, which is why validation and data quality run through the whole listing. Modelling and architecture discussions are there, but they lean on the cleaning work, not the other way round.
Revise SQL past joins and aggregations: window functions, CASE statements, GROUP BY used to hunt duplicates, string trimming and standardisation. Get straight what Bronze, Silver and Gold layers mean and why data gets split into them. In Snowflake, learn what a virtual warehouse and a stage are, since both sit under the pipelines this team builds. Streamlit appears in the responsibilities too, and it's a small tool to pick up.
Drafted with AI from Eaton's job posting and checked by Prashanth Jakkula before publishing. See our Editorial Policy.
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