Altak Group Inc.
Data Engineer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Data Engineer with a contract length of "Unknown", offering a pay rate of "$XX/hour". Key skills include Python, SQL, cloud platforms (AWS, Azure, GCP), and ETL/ELT pipeline development. A Bachelor's degree and 5+ years of experience are required.
🌎 - Country
United States
💱 - Currency
$ USD
-
💰 - Day rate
520
-
🗓️ - Date
August 12, 2026
🕒 - Duration
Unknown
-
🏝️ - Location
Unknown
-
📄 - Contract
Unknown
-
🔒 - Security
Unknown
-
📍 - Location detailed
United States
-
🧠 - Skills detailed
#Automation #Databases #Databricks #Datasets #Python #GCP (Google Cloud Platform) #Data Warehouse #Data Architecture #SQL (Structured Query Language) #Airflow #Data Quality #Spark (Apache Spark) #Data Management #AI (Artificial Intelligence) #Data Ingestion #ML (Machine Learning) #Monitoring #Kubernetes #Metadata #Kafka (Apache Kafka) #Data Lake #Azure #Cloud #Scala #Snowflake #Data Science #Version Control #Deployment #Data Engineering #AWS (Amazon Web Services) #Storage #Security #Computer Science #Data Catalog #Data Pipeline #Docker #NoSQL #"ETL (Extract #Transform #Load)" #GIT
Role description
Key Responsibilities
• Design, develop, and maintain scalable ETL/ELT pipelines for structured and unstructured data.
• Build and optimize data warehouses, data lakes, and cloud-based data platforms.
• Develop data architectures that support AI, machine learning, and advanced analytics use cases.
• Collaborate with Data Scientists, AI Engineers, and business stakeholders to understand data requirements.
• Create and maintain data ingestion frameworks from multiple sources including APIs, databases, cloud storage, and third-party systems.
• Ensure data quality, integrity, security, and governance across platforms.
• Monitor and optimize data pipeline performance, reliability, and scalability.
• Support AI model training and deployment by preparing and managing large datasets.
• Implement automation and orchestration workflows using modern data engineering tools.
• Build and maintain metadata management, data cataloging, and monitoring solutions.
• Troubleshoot production data issues and implement preventive solutions.
• Stay updated with emerging AI technologies, data engineering trends, and industry best practices.
• Design, develop, and maintain scalable ETL/ELT pipelines for structured and unstructured data.
• Build and optimize data warehouses, data lakes, and cloud-based data platforms.
• Develop data architectures that support AI, machine learning, and advanced analytics use cases.
• Collaborate with Data Scientists, AI Engineers, and business stakeholders to understand data requirements.
• Create and maintain data ingestion frameworks from multiple sources including APIs, databases, cloud storage, andthird-party systems.
• Ensure data quality, integrity, security, and governance across platforms.
• Monitor and optimize data pipeline performance, reliability, and scalability.
• Support AI model training and deployment by preparing and managing large datasets.
• Implement automation and orchestration workflows using modern data engineering tools.
• Build and maintain metadata management, data cataloging, and monitoring solutions.
• Troubleshoot production data issues and implement preventive solutions.
• Stay updated with emerging AI technologies, data engineering trends, and industry best practices.
Required Qualifications
• Bachelor's degree in Computer Science, Data Engineering, Information Systems, or a related field.
• 5+ years of professional experience in Data Engineering.
• Strong experience with Python and SQL.
• Experience building and maintaining ETL/ELT pipelines.
• Hands-on experience with cloud platforms such as AWS, Azure, or Google Cloud Platform.
• Strong knowledge of data warehousing concepts and modern data architectures.
• Experience working with large-scale datasets and distributed processing frameworks.
• Strong understanding of database technologies including SQL and NoSQL databases.
• Experience with workflow orchestration tools such as Airflow, Prefect, or similar platforms.
• Knowledge of version control systems such as Git.
• Excellent problem-solving and analytical skills.
Preferred Qualifications
• Experience with Spark, Databricks, Kafka, or Snowflake.
• Experience supporting MLOps and AI deployment workflows.
• Knowledge of containerization technologies such as Docker and Kubernetes.
• Experience with real-time streaming data pipelines.
• Relevant cloud certifications or data engineering certifications.
• Exposure to Generative AI, LLM applications, and AI agent frameworks
Key Responsibilities
• Design, develop, and maintain scalable ETL/ELT pipelines for structured and unstructured data.
• Build and optimize data warehouses, data lakes, and cloud-based data platforms.
• Develop data architectures that support AI, machine learning, and advanced analytics use cases.
• Collaborate with Data Scientists, AI Engineers, and business stakeholders to understand data requirements.
• Create and maintain data ingestion frameworks from multiple sources including APIs, databases, cloud storage, and third-party systems.
• Ensure data quality, integrity, security, and governance across platforms.
• Monitor and optimize data pipeline performance, reliability, and scalability.
• Support AI model training and deployment by preparing and managing large datasets.
• Implement automation and orchestration workflows using modern data engineering tools.
• Build and maintain metadata management, data cataloging, and monitoring solutions.
• Troubleshoot production data issues and implement preventive solutions.
• Stay updated with emerging AI technologies, data engineering trends, and industry best practices.
• Design, develop, and maintain scalable ETL/ELT pipelines for structured and unstructured data.
• Build and optimize data warehouses, data lakes, and cloud-based data platforms.
• Develop data architectures that support AI, machine learning, and advanced analytics use cases.
• Collaborate with Data Scientists, AI Engineers, and business stakeholders to understand data requirements.
• Create and maintain data ingestion frameworks from multiple sources including APIs, databases, cloud storage, andthird-party systems.
• Ensure data quality, integrity, security, and governance across platforms.
• Monitor and optimize data pipeline performance, reliability, and scalability.
• Support AI model training and deployment by preparing and managing large datasets.
• Implement automation and orchestration workflows using modern data engineering tools.
• Build and maintain metadata management, data cataloging, and monitoring solutions.
• Troubleshoot production data issues and implement preventive solutions.
• Stay updated with emerging AI technologies, data engineering trends, and industry best practices.
Required Qualifications
• Bachelor's degree in Computer Science, Data Engineering, Information Systems, or a related field.
• 5+ years of professional experience in Data Engineering.
• Strong experience with Python and SQL.
• Experience building and maintaining ETL/ELT pipelines.
• Hands-on experience with cloud platforms such as AWS, Azure, or Google Cloud Platform.
• Strong knowledge of data warehousing concepts and modern data architectures.
• Experience working with large-scale datasets and distributed processing frameworks.
• Strong understanding of database technologies including SQL and NoSQL databases.
• Experience with workflow orchestration tools such as Airflow, Prefect, or similar platforms.
• Knowledge of version control systems such as Git.
• Excellent problem-solving and analytical skills.
Preferred Qualifications
• Experience with Spark, Databricks, Kafka, or Snowflake.
• Experience supporting MLOps and AI deployment workflows.
• Knowledge of containerization technologies such as Docker and Kubernetes.
• Experience with real-time streaming data pipelines.
• Relevant cloud certifications or data engineering certifications.
• Exposure to Generative AI, LLM applications, and AI agent frameworks
