SoftStandard Solutions
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," located in "unknown." Key skills required include Python, SQL, Spark, and cloud technologies. Experience with ETL pipelines and big data tools is essential.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
Unknown
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🗓️ - Date
May 21, 2026
🕒 - Duration
Unknown
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🏝️ - Location
Unknown
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📄 - Contract
Unknown
-
🔒 - Security
Unknown
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📍 - Location detailed
United States
-
🧠 - Skills detailed
#AWS S3 (Amazon Simple Storage Service) #Compliance #MySQL #Hadoop #Data Integrity #Cloud #Programming #"ETL (Extract #Transform #Load)" #Data Integration #Databricks #Data Ingestion #AWS (Amazon Web Services) #Azure Data Factory #Data Architecture #Data Science #GCP (Google Cloud Platform) #Data Lake #Data Quality #Database Schema #Kafka (Apache Kafka) #AI (Artificial Intelligence) #Oracle #Snowflake #Scripting #PySpark #Scrum #SQL Queries #Data Privacy #Spark (Apache Spark) #MongoDB #Deployment #Jenkins #Kubernetes #ADF (Azure Data Factory) #Python #NoSQL #Azure #Data Engineering #Agile #Linux #Data Processing #S3 (Amazon Simple Storage Service) #Redshift #Scala #Security #SQL (Structured Query Language) #Batch #Monitoring #PostgreSQL #Data Warehouse #Version Control #Databases #ML (Machine Learning) #GIT #Airflow #Shell Scripting #Big Data #Docker
Role description
Data Engineer – Job Description
Responsibilities:
• Design, develop, and maintain scalable ETL/ELT pipelines for processing structured and unstructured data.
• Build and optimize data architectures, data lakes, and data warehouses for large-scale analytics.
• Develop data ingestion and transformation workflows using Python, SQL, Spark, and cloud technologies.
• Work with big data tools such as Hadoop, Spark, Kafka, Databricks, and Snowflake for real-time and batch data processing.
• Create and optimize complex SQL queries, stored procedures, and database schemas.
• Implement data quality, validation, cleansing, and monitoring processes to ensure data integrity.
• Collaborate with Data Scientists, Analysts, and Business teams to support reporting and AI/ML initiatives.
• Develop and maintain cloud-based data solutions on AWS/Azure/GCP environments.
• Automate workflows and deployments using CI/CD pipelines and version control tools.
• Monitor pipeline performance, troubleshoot issues, and optimize data processing efficiency.
• Ensure compliance with security, governance, and data privacy standards.
• Participate in Agile/Scrum ceremonies and contribute to end-to-end SDLC activities.
Required Skills:
• Strong programming experience in Python, SQL, and Shell scripting.
• Hands-on experience with Spark/PySpark, Kafka, Hadoop, Databricks, or Snowflake.
• Experience building ETL pipelines and data integration workflows.
• Knowledge of relational and NoSQL databases such as PostgreSQL, MySQL, MongoDB, Cassandra, or Oracle.
• Experience with cloud platforms like AWS, Azure, or GCP.
• Familiarity with Airflow, Jenkins, Git, Docker, and Kubernetes.
• Strong analytical, troubleshooting, and problem-solving skills.
Preferred Qualifications:
• Experience with real-time streaming and distributed processing systems.
• Knowledge of Data Warehousing concepts and dimensional modeling.
• Exposure to ML/Data Science workflows is a plus.
• Relevant cloud or big data certifications preferred.
Environment:
Python, SQL, PySpark, Kafka, Hadoop, Databricks, Snowflake, Airflow, AWS (S3, EMR, Glue, Redshift), Azure Data Factory, Docker, Kubernetes, Jenkins, Git, PostgreSQL, MongoDB, Linux, Agile/Scrum.
Data Engineer – Job Description
Responsibilities:
• Design, develop, and maintain scalable ETL/ELT pipelines for processing structured and unstructured data.
• Build and optimize data architectures, data lakes, and data warehouses for large-scale analytics.
• Develop data ingestion and transformation workflows using Python, SQL, Spark, and cloud technologies.
• Work with big data tools such as Hadoop, Spark, Kafka, Databricks, and Snowflake for real-time and batch data processing.
• Create and optimize complex SQL queries, stored procedures, and database schemas.
• Implement data quality, validation, cleansing, and monitoring processes to ensure data integrity.
• Collaborate with Data Scientists, Analysts, and Business teams to support reporting and AI/ML initiatives.
• Develop and maintain cloud-based data solutions on AWS/Azure/GCP environments.
• Automate workflows and deployments using CI/CD pipelines and version control tools.
• Monitor pipeline performance, troubleshoot issues, and optimize data processing efficiency.
• Ensure compliance with security, governance, and data privacy standards.
• Participate in Agile/Scrum ceremonies and contribute to end-to-end SDLC activities.
Required Skills:
• Strong programming experience in Python, SQL, and Shell scripting.
• Hands-on experience with Spark/PySpark, Kafka, Hadoop, Databricks, or Snowflake.
• Experience building ETL pipelines and data integration workflows.
• Knowledge of relational and NoSQL databases such as PostgreSQL, MySQL, MongoDB, Cassandra, or Oracle.
• Experience with cloud platforms like AWS, Azure, or GCP.
• Familiarity with Airflow, Jenkins, Git, Docker, and Kubernetes.
• Strong analytical, troubleshooting, and problem-solving skills.
Preferred Qualifications:
• Experience with real-time streaming and distributed processing systems.
• Knowledge of Data Warehousing concepts and dimensional modeling.
• Exposure to ML/Data Science workflows is a plus.
• Relevant cloud or big data certifications preferred.
Environment:
Python, SQL, PySpark, Kafka, Hadoop, Databricks, Snowflake, Airflow, AWS (S3, EMR, Glue, Redshift), Azure Data Factory, Docker, Kubernetes, Jenkins, Git, PostgreSQL, MongoDB, Linux, Agile/Scrum.
