7 часов назад
Data Engineer (Integrations & Data Quality)
Вакансия из Hirify Global, списка международных tech-компанийДля мэтча и отклика нужен Plus
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
Data Engineer (Integrations & Data Quality) (Python/SQL/dbt): Building ingestion pipelines and trustworthy data models for online retail data products with an accent on third-party integrations, source investigation, and data quality. Focus on identifying root causes in messy vendor data, reviewing AI-generated code, and shipping documented integrations under tight customer timelines.
Location: Remote in Europe and South America
Company
builds data products for online retail brands.
What you will do
- Build ingestion pipelines from third-party APIs and vendor systems.
- Investigate new data sources, identify nulls, inconsistent types, and unusual behaviors, and determine root causes.
- Model and test data in dbt to produce clean, trustworthy datasets.
- Direct AI coding agents and critically review the code they generate.
- Deliver new integrations within customer timelines and document source-specific quirks.
Requirements
- 5+ years of data engineering experience, with mid-to-senior-level capability.
- Strong Python and SQL skills.
- Hands-on experience with dbt.
- Experience building pipelines from third-party APIs, ideally with dlt; Fivetran or Airbyte experience is also relevant.
- Experience untangling messy, poorly documented source data and using AI coding agents day to day.
- Strong spoken and written English, plus a degree in Computer Science, Engineering, or a related field, or equivalent experience.
Nice to have
- E-commerce or DTC data experience, including store platforms, advertising platforms, or marketing tools.
- Experience with DuckDB or MotherDuck.
- Experience with Temporal or similar orchestration tools.
Culture & Benefits
- Permanent, full-time remote position.
- Work independently and pick up new systems without extensive hand-holding.
- Operate at startup pace while maintaining data quality.
- Communicate clearly about data issues and their impact.
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