Data engineer in fintech

Weltrade; United States; Remote; USD 120,000 - 170,000; Full time

Benefits offered by this job

Полностью удалённая работа

Глобальная команда

Job summary

Weltrade ищет Data Engineer для владения хранилищем данных и ELT-пайплайнами, powering analytics, атрибуцию маркетинга и отчеты продукта. Вы будете проектировать масштабируемые модели данных и инфраструктуру в BigQuery с dbt и Airflow.

Работа с аналитиками и стейкхолдерами, обеспечение качества данных, оптимизация производительности и экономии затрат, документирование lineage и моделей. Мы полностью удалённые и глобальная cross-functional команда, участие в росте и клиентском опыте.

Qualifications

• Сильный SQL и Python; опыт работы с BigQuery.
• Опыт dbt: моделирование, тесты, макросы.
• Опыт оркестрации Airflow.
• Моделирование данных: Kimball, Inmon, Data Vault, Anchor modeling.

Responsibilities

• Проектирование и обслуживание моделей данных и DWH: raw -> staging -> core -> mart в BigQuery.
• Разработка и оркестрация ELT-пайплайнов с dbt и Airflow, инкрементальные загрузки, откат/обновления.
• Обеспечение качества данных, мониторинг, тестирование; работа с источниками и линиями данных.
• Документация lineage и моделей; взаимодействие с аналитиками и стейкхолдерами.

Job description

Описание: As a Data Engineer, you will own the company's data warehouse and the pipelines that power analytics, marketing attribution, and product reporting. You'll design and maintain scalable data models and ELT pipelines in BigQuery using dbt and Airflow, transforming raw data into reliable, business-ready datasets. Working closely with analysts and stakeholders, you'll ensure data quality, optimize performance, and help build a trusted foundation for data-driven decision-making.

Задачи: Design, build, and maintain data models and the DWH layering (raw -> staging -> core -> mart) in BigQuery. Develop and orchestrate ELT pipelines with dbt and Airflow — incremental loads, idempotent/re-runnable jobs, backfills. Model data using established methodologies (Kimball, Inmon, Data Vault, Anchor modeling) and choose the right approach per use case. Implement data quality checks, tests, and monitoring; investigate and fix pipeline failures. Manage schema evolution and breaking changes from upstream sources. Optimize warehouse performance and cost (partitioning, clustering, query tuning). Document data lineage and models; collaborate with analysts and stakeholders on the source of truth for metrics.

Требования: Strong SQL (joins, window functions, delta/change detection) and solid Python. Hands-on experience with BigQuery (or a comparable cloud DWH). Production experience with dbt: models, tests, macros. Orchestration experience with Airflow (or equivalent). Data modeling expertise: Kimball / Inmon / Data Vault / Anchor modeling, and reasoning about layer boundaries and trade-offs. ELT/ETL design: incremental extraction, backfills, schema evolution. Experience with data quality, testing, and pipeline monitoring.

Будет плюсом: Integration with ad-platform APIs / advertising cabinets (Google Ads, Facebook/Meta) and marketing attribution data; Experience building AI/LLM data pipelines; Experience building agents / agentic workflows; Product analytics exposure (AppsFlyer, GTM, server-to-server, enhanced conversions); CI/CD for data pipelines

Условия: We operate fully remotely and offer a global and cross-functional environment. A chance to own ideas and make a measurable impact on organizational growth and client experience. Well-balanced leave allowance (paid vacation, sick leave, public holidays, etc.) and a supportive culture that values every employee.