4 дня назад
Big Data Engineer
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
Текст:
TL;DR
Big Data Engineer (Spark/Kafka/Snowflake): Developing scalable, high-performance big data infrastructure, data pipelines, warehouses, and analytics platforms with an accent on cloud and on-premise computing. Focus on designing data warehouses, integrating managed cloud services, and supporting BI and ML platforms.
Location: Remote within Latin America: Argentina, Uruguay, Colombia, Chile, or the Dominican Republic
Compensation: USD remuneration; amount not specified
Company
is a San Francisco-based software development firm specializing in AI software development, data, cloud, generative AI, intelligent automation, and machine learning solutions.
What you will do
- Design and develop scalable, high-performance big data infrastructure for on-premise and cloud environments.
- Build and enhance data pipelines, data services, data warehouses, BI platforms, and ML platforms.
- Work with Spark, Kafka, Snowflake, or similar big data frameworks.
- Manipulate, analyze, and visualize data to support analytics initiatives.
- Collaborate with engineers and contribute to solving complex technical problems.
Requirements
- Bachelor’s or master’s degree in computer science, a related field, or equivalent experience.
- 5+ years of experience in data-related and data management responsibilities.
- Deep expertise in designing and building data warehouses and big data analytics systems.
- Practical experience manipulating, analyzing, and visualizing data.
- Professional English proficiency at B2/C1 level.
- Self-driven approach, strong work ethic, and passion for problem solving.
Nice to have
- Experience with Airflow, Glue, Elastic Stack, Amazon Redshift, Snowflake, BigQuery, Azure SQL Database, EMR, Azure, Databricks, Altiscale, or Qubole.
- Experience using Jupyter, Google Colab, or similar notebooks.
Culture & Benefits
- 100% remote work.
- Paid time off and U.S. holidays.
- Mentored career development.
- USD remuneration and profit sharing.
- Maternity coverage.
- Opportunities for continuous learning, open-source initiatives, philanthropy, and knowledge sharing.
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