Gazelle Global
AWS Data Engineer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for an AWS Data Engineer on a contract basis, requiring strong experience in PySpark, SQL, and AWS services. The position involves designing scalable data pipelines and implementing data governance. Advanced Python skills are essential.
🌎 - Country
United Kingdom
💱 - Currency
£ GBP
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💰 - Day rate
Unknown
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🗓️ - Date
May 16, 2026
🕒 - 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
London Area, United Kingdom
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🧠 - Skills detailed
#Lambda (AWS Lambda) #Cloud #Data Quality #Spark (Apache Spark) #SQL (Structured Query Language) #Data Processing #Scala #Data Pipeline #Airflow #Data Governance #Data Engineering #PySpark #Athena #Automation #Apache Airflow #Business Analysis #Batch #AWS (Amazon Web Services) #S3 (Amazon Simple Storage Service) #Python #Security #"ETL (Extract #Transform #Load)"
Role description
Your responsibilities:
• Design, develop, and maintain scalable data pipelines on AWS using Glue, EMR, S3, and Athena for batch and real-time processing.
• Build and optimize ETL workflows using PySpark and SQL, ensuring high data quality, reliability, and performance. Orchestrate and schedule data pipelines using Apache Airflow, enabling seamless data movement across systems.
• Collaborate with business analysts and stakeholders to translate data requirements into technical solutions and deliver actionable insights.
• Implement data governance, security, and best practices while working within cloud-native architectures on AWS.
Your Profile
Essential skills/knowledge/experience:
• Strong experience with PySpark, distributed data processing, and largescale ETL/ELT pipelines.
• Advanced proficiency in Python for data engineering, automation
• Hands‑on expertise with AWS services (S3, Glue, Lambda, EMR, Bedrock / custom model hosting).
• Hands-on experience in SQL and ETL.
Desirable skills/knowledge/experience:
• Pyspark, Python, SQL, AWS
Your responsibilities:
• Design, develop, and maintain scalable data pipelines on AWS using Glue, EMR, S3, and Athena for batch and real-time processing.
• Build and optimize ETL workflows using PySpark and SQL, ensuring high data quality, reliability, and performance. Orchestrate and schedule data pipelines using Apache Airflow, enabling seamless data movement across systems.
• Collaborate with business analysts and stakeholders to translate data requirements into technical solutions and deliver actionable insights.
• Implement data governance, security, and best practices while working within cloud-native architectures on AWS.
Your Profile
Essential skills/knowledge/experience:
• Strong experience with PySpark, distributed data processing, and largescale ETL/ELT pipelines.
• Advanced proficiency in Python for data engineering, automation
• Hands‑on expertise with AWS services (S3, Glue, Lambda, EMR, Bedrock / custom model hosting).
• Hands-on experience in SQL and ETL.
Desirable skills/knowledge/experience:
• Pyspark, Python, SQL, AWS
