2 часа назад
Data Engineer (GCP/BigQuery)
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 (GCP/BigQuery): Building and evolving a scalable data platform across analytics engineering, data pipelines, data quality, and ML data preparation with an accent on BigQuery, Dataform, Python, and GCP services. Focus on designing reliable transformations, orchestrating and monitoring production workflows, and moving data science prototypes into maintainable pipelines.
Location: Remote, Dublin, Ireland
Company
develops data platforms and analytics solutions for sports performance.
What you will do
- Build and maintain BigQuery data models with Dataform using Bronze, Silver, and Gold medallion architecture patterns.
- Write performant SQL transformations and contribute to Looker dashboards and LookML models.
- Develop tested Python data pipelines with Git, linting, and CI/CD integration.
- Schedule and monitor workflows with Cloud Composer, Airflow, and related orchestration tools.
- Implement data quality checks, alerting, observability, freshness monitoring, and anomaly detection.
- Support Data Scientists by productionizing notebook work and contributing to feature pipelines for ML workloads.
Requirements
- Strong SQL skills and hands-on BigQuery experience.
- Experience with Dataform or dbt and version-controlled data transformation.
- Python engineering experience with clean, tested, and linted code.
- Familiarity with GCP, especially BigQuery; Cloud Storage, Cloud Run, Pub/Sub, and Datastream are advantageous.
- Experience with Cloud Composer, Airflow, or a comparable orchestration tool.
- Knowledge of dimensional modelling, Kimball principles, or medallion architecture, plus basic Docker containerisation skills.
Nice to have
- Looker or LookML experience.
- Knowledge of CDC tools and concepts, including Datastream or Debezium.
- Exposure to scikit-learn, MLflow, Vertex AI, or other MLOps tooling.
- AWS experience with services such as Redshift, Glue, or RDS.
- Interest in sports performance data.
Culture & Benefits
- Full-time remote work.
- Collaboration with Engineering, Data Science, Product, Analytics, and senior engineering stakeholders.
- Pragmatic use of AI-assisted development to improve productivity and code quality.
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