Compunnel Inc.
Data Engineer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Senior Data Engineer (AWS) with a contract length of "Unknown", offering a pay rate of "Unknown". Key skills required include AWS services (S3, Glue, Redshift), Python, SQL, and data architecture experience.
🌎 - Country
United States
💱 - Currency
$ USD
-
💰 - Day rate
Unknown
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🗓️ - Date
November 13, 2025
🕒 - Duration
Unknown
-
🏝️ - Location
Unknown
-
📄 - Contract
Unknown
-
🔒 - Security
Unknown
-
📍 - Location detailed
North Carolina, United States
-
🧠 - Skills detailed
#IAM (Identity and Access Management) #ML (Machine Learning) #Data Warehouse #S3 (Amazon Simple Storage Service) #Normalization #Data Engineering #Data Lineage #PySpark #Data Modeling #Scala #Data Quality #Observability #Strategy #Data Lake #Data Ingestion #Jenkins #Snowflake #Airflow #SQL (Structured Query Language) #AWS (Amazon Web Services) #DevOps #Athena #Code Reviews #Batch #"ETL (Extract #Transform #Load)" #Terraform #Python #Redshift #Docker #Data Pipeline #GitHub #Security #Spark (Apache Spark) #Lambda (AWS Lambda) #Infrastructure as Code (IaC) #Programming #Data Architecture
Role description
💼 Senior Data Engineer (AWS)
Overview
A Senior Data Engineer designs, builds, and optimizes large-scale data systems on AWS. They lead the data infrastructure strategy, mentor junior engineers, and ensure that the company’s data is secure, scalable, and analytics-ready.
Key Responsibilities
• Architect and implement end-to-end data pipelines using AWS services (Glue, Lambda, Step Functions, Kinesis, Redshift, S3, EMR, etc.).
• Design data lake and data warehouse solutions with performance, cost, and scalability in mind.
• Build and manage real-time and batch data ingestion frameworks.
• Optimize ETL/ELT workflows, ensuring reliability and quality.
• Collaborate with analytics, ML, and business teams to define data models and governance standards.
• Implement infrastructure as code (IaC) using tools like Terraform or AWS CDK.
• Establish data quality, lineage, and observability best practices.
• Mentor junior data engineers and lead code reviews.
Required Skills
• AWS Expertise:
• Deep experience with S3, Glue, Athena, Redshift, Lambda, EMR, Kinesis, and IAM.
• Programming:
• Proficient in Python, SQL, and PySpark.
• Data Architecture:
• Experience designing scalable data lakes, warehouses, and pipelines.
• DevOps / CI-CD:
• Familiarity with Docker, GitHub Actions, or Jenkins.
• Workflow Orchestration:
• Airflow, Step Functions, or similar tools.
• Data Modeling:
• Experience with star/snowflake schema, normalization, and partitioning strategies.
• Security & Governance:
• Implement encryption, access control, and data lineage tracking.
💼 Senior Data Engineer (AWS)
Overview
A Senior Data Engineer designs, builds, and optimizes large-scale data systems on AWS. They lead the data infrastructure strategy, mentor junior engineers, and ensure that the company’s data is secure, scalable, and analytics-ready.
Key Responsibilities
• Architect and implement end-to-end data pipelines using AWS services (Glue, Lambda, Step Functions, Kinesis, Redshift, S3, EMR, etc.).
• Design data lake and data warehouse solutions with performance, cost, and scalability in mind.
• Build and manage real-time and batch data ingestion frameworks.
• Optimize ETL/ELT workflows, ensuring reliability and quality.
• Collaborate with analytics, ML, and business teams to define data models and governance standards.
• Implement infrastructure as code (IaC) using tools like Terraform or AWS CDK.
• Establish data quality, lineage, and observability best practices.
• Mentor junior data engineers and lead code reviews.
Required Skills
• AWS Expertise:
• Deep experience with S3, Glue, Athena, Redshift, Lambda, EMR, Kinesis, and IAM.
• Programming:
• Proficient in Python, SQL, and PySpark.
• Data Architecture:
• Experience designing scalable data lakes, warehouses, and pipelines.
• DevOps / CI-CD:
• Familiarity with Docker, GitHub Actions, or Jenkins.
• Workflow Orchestration:
• Airflow, Step Functions, or similar tools.
• Data Modeling:
• Experience with star/snowflake schema, normalization, and partitioning strategies.
• Security & Governance:
• Implement encryption, access control, and data lineage tracking.
