Lead Data Engineer - Python

  • Closed
  • US Company | Medium (51-250 employees)
  • LATAM (100% remote)
  • 7+ years
  • Full-time (40h)
  • Finance
  • Full Remote

Required skills

  • Python
  • AWS
  • SQL
  • NoSQL
  • Apache Spark

Requirements

Must-haves

  • 7+ years of experience in data engineering
  • Leadership experience
  • AWS experience
  • Apache Spark experience
  • Experience building scalable data pipelines and ETL processes from scratch
  • Strong proficiency with Python
  • Proficiency with SQL and relational database technologies
  • Expertise in distributed systems and data processing frameworks
  • Expertise in data lake and cloud computing platforms
  • Deep knowledge of data modeling and data warehousing concepts
  • Familiarity with data governance, access controls, security, and compliance principles
  • Ability to optimize data pipelines for performance, scalability, and reliability
  • Strong problem-solving and analytical skills for complex data engineering challenges
  • Excellent communication skills in both spoken and written English
  • Bachelor's Degree in Computer Engineering, Computer Science, or equivalent

Nice-to-haves

  • Experience with real-time data processing and streaming frameworks
  • Knowledge of modern data lake house technologies
  • Experience with Docker and Kubernetes
  • Experience with Terraform, Airflow, Pandas, PySpark
  • Experience with NoSQL databases
  • Experience with data visualization tools and data exploration techniques
  • Actively participating in the data engineering community
    (e.g. making contributions to open-source data engineering projects)

What you will work on

  • Develop effective data pipelines and ETL processes for data lake integration
  • Optimize data infrastructure considering data volume, velocity, and variety
  • Ensure data architecture performance, reliability, and scalability
  • Implement data governance practices to ensure data quality, integrity, and security
  • Adopt optimal engineering practices, methodologies, procedures, and technologies
  • Work with cross-functional teams to develop data solutions that meet business needs
  • Stay updated with data engineering trends
  • Share insights with the team and organization
  • Effectively communicate technical concepts, solutions, and recommendations to both technical and non-technical stakeholders

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