Intone Networks Inc
Data Engineer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Data Engineer with a contract length of "unknown" and a pay rate of "unknown." Key skills include Python, PySpark, Spark, AWS services, and experience in cloud-based environments. Industry experience in data engineering is required.
🌎 - Country
United States
💱 - Currency
Unknown
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💰 - Day rate
Unknown
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🗓️ - Date
November 14, 2025
🕒 - Duration
Unknown
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🏝️ - Location
Unknown
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📄 - Contract
Unknown
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🔒 - Security
Unknown
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📍 - Location detailed
McLean, VA
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🧠 - Skills detailed
#S3 (Amazon Simple Storage Service) #Databricks #Data Quality #Redshift #Athena #Data Lifecycle #Cloud #Jenkins #Airflow #Spark (Apache Spark) #Data Lake #Data Ingestion #SQL (Structured Query Language) #Process Automation #Storage #Version Control #Terraform #Agile #PySpark #Data Engineering #Automation #"ETL (Extract #Transform #Load)" #Python #AWS Glue #Lambda (AWS Lambda) #DevOps #GIT #AWS (Amazon Web Services) #Scala
Role description
Key Responsibilities:
• Design, build, and optimize ETL pipelines using Python, PySpark, and Spark
• Develop scalable data solutions leveraging Databricks, AWS Glue, EMR, and S3
• Collaborate with cross-functional engineering and analytics teams to implement best practices in data ingestion, transformation, and storage
• Support data quality, performance tuning, and process automation across the data lifecycle
• Work in Agile environments with CI/CD and version control tools Required Skills and Experience:
• 3 to 7 plus years of experience in data engineering, preferably in cloud-based environments
• Strong proficiency in Python, PySpark, Spark, and SQL
• Hands-on experience with AWS data services (S3, Glue, EMR, Redshift, Lambda, Athena)
• Experience with Databricks or equivalent data lake platforms
• Familiarity with modern DevOps practices (Git, Jenkins, Terraform, Airflow, etc.)
Key Responsibilities:
• Design, build, and optimize ETL pipelines using Python, PySpark, and Spark
• Develop scalable data solutions leveraging Databricks, AWS Glue, EMR, and S3
• Collaborate with cross-functional engineering and analytics teams to implement best practices in data ingestion, transformation, and storage
• Support data quality, performance tuning, and process automation across the data lifecycle
• Work in Agile environments with CI/CD and version control tools Required Skills and Experience:
• 3 to 7 plus years of experience in data engineering, preferably in cloud-based environments
• Strong proficiency in Python, PySpark, Spark, and SQL
• Hands-on experience with AWS data services (S3, Glue, EMR, Redshift, Lambda, Athena)
• Experience with Databricks or equivalent data lake platforms
• Familiarity with modern DevOps practices (Git, Jenkins, Terraform, Airflow, etc.)
