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Remote Data Engineer

Roles & Responsibilities

  • 10 years experience
  • Must be local top Santa Clara
  • Extensive hands-on experience in cloud environments with GCP focus
  • Expert-level skills in Python and advanced SQL

Requirements:

  • Design, implement, and optimize analytical datasets for reporting and data science
  • Architect, build, and maintain scalable data pipelines
  • Implement and support Big Data solutions for batch and streaming analytics
  • Collaborate with product managers and business teams to understand data requirements

Job description

Remote Data Engineer needs 10 years experience

Remote Data Engineer requires:

Must be local top Santa Clara

Cloud Platform Expertise (GCP Focus): Extensive hands-on experience working in dynamic cloud environments, with a strong preference for Google Cloud Platform (GCP) services, specifically:

BigQuery: Expert-level skills in data ingestion, performance optimization, and data modeling within a petabyte-scale environment.

Experience with other relevant GCP services like Cloud Storage, Cloud Dataflow/Beam, or Pub/Sub

Programming & Querying:

Python: Expert-level programming proficiency in Python, including experience with relevant data engineering libraries

SQL: A solid command of advanced SQL for complex querying, data processing, and performance tuning.

Data Pipeline Orchestration: Prior experience using workflow management and orchestration tools (e.g., Apache Airflow, Cloud Composer, Airflow,Dagster, or similar).

DevOps/CI/CD: Experience with version control (Git) and familiarity with CI/CD practices and tools (e.g., GitLab, GitHub Actions) to automate deployment and testing processes.

DevOps/CI/CD, Data Pipeline Orchestration:, SQL, Cloud Platform Expertise (GCP Focus):, Cloud Storage, Cloud Dataflow/Beam, or Pub/Sub

Remote Data Engineer duties:

Design, implement, and optimize clean, well-structured, and performant analytical datasets to support high-volume reporting, business analysis, and data science model development.

Architect, build, and maintain scalable and robust data pipelines for diverse applications, including business intelligence, advanced analytics

Implement and support Big Data solutions for both batch (scheduled) and real-time/streaming analytics.

Work closely with product managers and business teams to understand data requirements and translate them into technical solutions.

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