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22 часа назад

Data Engineer

Формат работы
remote (Global)
Тип работы
fulltime
Грейд
junior/middle
Английский
b2
Вакансия из списка Hirify.GlobalВакансия из Hirify Global, списка международных tech-компаний
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Описание вакансии

Текст:
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TL;DR

Data Engineer: Cleaning, transforming, and preparing complex data for integration into machine learning models with an accent on data hygiene, reconciliation, and transformation. Focus on building data transformation and validation pipelines, resolving data discrepancies, and ensuring data accuracy across systems.

Company

hirify.global Labs is committed to equal employment opportunity and non-discrimination for all employees and qualified applicants.

What you will do

  • Collaborate with client teams to understand data sources, formats, and integration requirements.
  • Clean, reconcile, and transform raw customer data to align with the platform’s ingestion standards.
  • Build and maintain data transformation and validation pipelines to ensure consistent, high-quality outputs.
  • Diagnose and resolve data discrepancies, ensuring accuracy and consistency across systems.
  • Develop scripts and workflows for repeatable data processing and format standardization.
  • Partner with internal engineering and client success teams to deliver clean, production-ready datasets.

Requirements

  • 1–3 years of experience in a data-focused role.
  • Bachelor’s degree in Computer Science, Information Systems, Statistics, or a related field preferred.
  • Strong proficiency in Python, particularly NumPy and Pandas for data manipulation and transformation.
  • Solid experience with SQL for data querying, joining, and reconciliation across multiple sources.
  • Experience building or maintaining ETL/ELT pipelines or data wrangling workflows.
  • Excellent communication skills for collaborating with both technical and non-technical stakeholders.

Nice to have

  • Experience with data validation, schema alignment, or reconciliation across disparate systems.
  • Familiarity with version control, testing, or other software engineering best practices.
  • Exposure to data visualization or reporting tools for validating and communicating results.
  • Background in client-facing or consulting environments where data integrity and delivery were key.

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