Job Title: Data Engineer Role Overview We are looking for high‑potential early‑career engineers who are strong in fundamentals and comfortable with AI‑assisted development. This role focuses on building scalable data pipelines while learning and growing in a fast‑paced, cloud‑native environment. We are specifically targeting candidates from top‑tier universities or those with internship/work experience at leading technology companies, with strong problem‑solving and learning ability. Key Responsibilities Build and support ETL/ELT pipelines using Databricks, dbt, Snowflake, and Azure. Write efficient SQL and Python for data processing and transformations. Leverage AI‑assisted development tools (Claude, Copilot) for coding and debugging. Ensure data quality, reliability, and pipeline monitoring. Collaborate with reporting and engineering teams to meet data needs. Participate in Agile/Scrum ceremonies and deliver sprint commitments. Support production operations and incident triage. Required Qualifications Bachelor's degree in Computer Science, Engineering, or related field. 4 to 6 years of experience (including internships or projects). Strong fundamentals in SQL and Python. Familiarity with cloud platforms (Azure preferred) and data concepts. Experience with Git/version control and collaborative development. Exposure to AI coding tools (Claude, GitHub Copilot, etc.). Strong communication skills and ability to learn quickly. Preferred Profile (Key Differentiator) Graduates from top‑tier universities (e.g., MIT, Stanford, CMU, UC Berkeley, or equivalent). Candidates with experience/internships at leading tech companies (Amazon, Google, Meta, etc.). Demonstrated strong problem‑solving ability and learning agility, even with limited hands‑on experience. Nice to have: Exposure to Databricks, dbt, Snowflake (projects/coursework acceptable). Experience with Azure Data Factory / Data Lake. Hands‑on project experience using AI‑assisted development. Why Join Work on foundational data systems supporting high‑impact business initiatives, while gaining hands‑on experience with modern data stack and AI‑driven engineering practices. #J-18808-Ljbffr
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