Data Engineer

India - Hyderabadmid

Data EngineerData

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Quick Summary

Overview

Career CategoryClinical Job Description Data Science Engineer GCF Level 4 As a Data Science Engineer, you will develop and test assigned data pipelines,

Technical Tools

Data EngineerData

Clinical

Responsibilities

~1 min read

→Work with data scientists to understand user needs and technical requirements.
→Implement assigned API, retrieval or ML components with guidance.
→Prepare permitted datasets and record sources and versions for assigned analytics and AI workflows.
→Develop and test pipeline components and approved AI integrations using Python, SQL and version control.
→Run specified regression and failure-case tests, investigate issues and document results.
→Review AI-assisted code and record reproducible results.
→Collaborate across regions with Data Science, Clinical Development, IS and data-platform partners.
→Support acceptance testing and document AI limitations and required human review.
→Support delivered products, identify improvements and prepare technical documentation, handoffs and training materials.

Deliver well-tested, documented components that meet assigned technical requirements.
Follow data-access and privacy rules, protect secrets and escalate unsafe AI behavior or data exposure.
Contribute reusable components and improvements to data pipelines and analytical tools.
Take ownership of agreed deliverables and raise issues affecting quality or timelines.
Maintain code quality, maintainability and reliability.
Deliver tested components and support documentation on time.
Recommend implementation options and make code, testing and documentation decisions within agreed standards.

Requirements

~1 min read

5 to 9 years of experience in Data Engineer

Master’s degree in Computer Science, Data Science, Informatics, Software Engineering, Statistics or another technical discipline.
Internship or early-career experience in the pharmaceutical or high-tech industry.
Strong programming skills in Python and/or R, with familiarity with SQL.
Familiarity with version control, software testing and documentation.
Familiarity with data structures, pipelines, APIs and cloud environments; approved AI integrations where relevant.
Basic ML and generative AI literacy and the ability to learn approved tools with review.
Working knowledge of software design, testing, debugging and maintainable code.
Ability to run specified AI tests and record model, prompt and data versions.
Ability to handle permitted data securely and recognize privacy, access and unsafe-AI-behavior risks.
Learning agility, structured problem-solving, accountability and effective collaboration.
Ability to explain software behavior, data flows and technical issues clearly to the intended audience.