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Kiwi
Kiwi
51 – 200 Xodimlar
H1B vizasi homiysiFintexKonsaltingSug‘urta
Kiwi shaxsiy kreditlash va kredit tarixini shakllantirishga ixtisoslashgan fintech kompaniyadir. Uning raqamli platformasi mijozlarga kredit reytingiga ta’sir qilmagan holda 3 000 AQSh dollarigacha bo‘lgan kreditlar uchun onlayn ariza topshirish imkonini beradi, shuningdek, moslashuvchan qaytarish variantlari va avtomatik to‘lovlarni taklif etadi. Kiwi talablarga javob beradigan to‘lovlar haqida kredit byurolariga ham ma’lumot berib, mijozlarga mustahkamroq kredit tarixini yaratishga yordam beradi, ma’lumotlarni himoya qilish esa kreditlash jarayoniga integratsiya qilingan.
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Kompaniyadagi ish o‘rinlari

Credit Risk Data Engineer at Kiwi – Remote in Dominican Republic

Own Kiwi’s credit-risk data foundation for lending decisions, from Snowflake pipelines and data quality monitoring to reproducible modeling datasets. This remote role supports teams across the Dominican Republic, Argentina, and Colombia.

ARArgentinaCOKolumbiyaDODominikan RespublikasiMasofaviyTo‘liq stavkaO‘rta darajaSeniorTalab Qilinadigan Til: inglizchaMa’lumotlar muhandisi
AirflowBulutPythonSQL

Tavsif

  • Manage the complete inventory of credit data sources, including TransUnion, Clarity, Plaid, Prism, and internal application and loan-servicing systems
  • Specify the required data, grain, freshness, and destination for each source
  • Create data contracts with engineers responsible for vendor integrations
  • Verify that raw vendor responses are fully retained for model development and audit needs
  • Monitor source and model-input freshness, volume, fill rate, and schema
  • Configure alerts for declining feature fill rates, missing vendor fields, and unusual volume changes
  • Monitor production-model input drift in partnership with data scientists
  • Investigate data incidents, determine root causes, coordinate remediation, and track resolution
  • Own the Credit Risk layer of the Snowflake warehouse across raw, staging, mart, and feature tables
  • Develop and maintain pipelines and transformations for application, loan, performance, and vendor data
  • Ensure modeling datasets remain reproducible and protected from training-data leakage
  • Maintain the tables that power Credit Risk dashboards and governed metrics
  • Develop automated checks for keys, duplicates, value ranges, referential integrity, and source reconciliation
  • Keep documentation, data lineage, and ownership registries current
  • Support vendor governance by reviewing SLAs and reconciling pull volumes with vendor invoices

Talablar

  • At least four years of data engineering or analytics engineering experience, including primary ownership of a production data platform
  • Expert-level SQL and strong data modeling capabilities, including dimensional models, slowly changing data, and point-in-time snapshots
  • Practical experience with a cloud data warehouse, preferably Snowflake
  • Experience with a transformation framework such as dbt, along with version control, code review, and CI practices
  • Python experience for building pipelines, tests, and automation
  • Demonstrated success implementing data-quality tests and alerting with dbt tests, Great Expectations, Monte Carlo, Elementary, or custom checks
  • Experience using an orchestration platform such as Airflow, Dagster, or Prefect
  • A strong ownership approach, including proactively detecting issues, tracing root causes, and driving them to resolution
  • Clear written communication skills for producing dependable data contracts, incident notes, and table documentation
  • Spanish and English fluency is helpful but not required
  • Experience with fintech or lending, credit bureau and bank data, feature tables, training datasets, model monitoring, lending compliance, BI tools, or Metabase is helpful but not required

Imtiyozlar

  • Work on important financial products with a direct effect on customers and business growth
  • Take full ownership of Kiwi’s Credit Risk data layer and influence its development
  • Tackle substantive problems across pipelines, data quality, monitoring, and modeling datasets
  • Join a workplace where AI is becoming central to how the team works and builds
  • Collaborate with colleagues across Credit Risk, Data Science, Engineering, and Product
  • Work fully remotely

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