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Cube Wealth logo — Cube Wealth Data Engineer Intern in Kurnool, Andhra Pradesh
Cube Wealth VerifiedActively Hiring

Data Engineer Intern

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Location

Kurnool, Andhra Pradesh

Work Mode

Hybrid

Duration

3 months

Stipend

₹22,000/mo

Start Date

Immediate / Rolling

Openings

2 positions

Type

Full-time

Category

Data Science

Apply Now — ₹22,000/moApply on Company Website ↗

About the Internship

Join Cube Wealth in Kurnool as a Data Engineer Intern. This entry-level internship is designed for students and recent graduates who learn best by building, shipping and getting real feedback on data engineering and large-scale pipelines work. About the role As a Data Engineer Intern at Cube…

About Cube Wealth

Cube Wealth offers a mentor-led Data Engineer Intern in Kurnool with live project work in data science, a stipend of ₹22,000/month and a Pre-Placement Offer opportunity for strong performers.
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Kurnool, Andhra PradeshVerified employer2 openings
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Skills Required

PySparkDatabricksSnowflakeApache KafkaETL Pipeline DevelopmentAdvanced SQLApache AirflowData Modelling & WarehousingPythonCloud Storage (S3 / ADLS / GCS)

Who Can Apply

  • Final-year students and recent graduates from B.E./B.Tech (CSE/IT), BCA, MCA or M.Sc (Computer Science) who are based in or willing to work from Kurnool. You should be keen to learn PySpark, Databricks, Snowflake on real projects and able to write clearly. Freshers are welcome — no prior work experience is required, and a strong project portfolio matters more than a long CV.

Perks

Flexible working hoursNetworking with senior professionalsInternship CertificateLetter of RecommendationMentorship from a named mentor

Interview & Selection Process

4 rounds — typical selection flow for this role:

  1. 1Application screening & resume shortlisting by the talent team
  2. 2Discussion with the hiring manager on the role and your fit
  3. 3Team-fit / behavioural interview with the team
  4. 4HR discussion — stipend, start date, expectations and offer
22,000/month

Stipend · 3 months

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Openings2 positions
Applicants0 applied
ModeHybrid
CategoryData Science
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