SoftStandard Solutions
Data Engineer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Data Engineer with 4–5 years of experience in Python and SQL, focusing on data pipelines and ETL/ELT workflows. Contract length is unspecified, with a competitive pay rate. Experience with cloud platforms and relevant certifications preferred.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
Unknown
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🗓️ - Date
August 12, 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
United States
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🧠 - Skills detailed
#Automation #Databases #Databricks #PostgreSQL #Python #GCP (Google Cloud Platform) #NumPy #PySpark #ADF (Azure Data Factory) #SQL (Structured Query Language) #REST API #Data Processing #Airflow #Data Quality #Spark (Apache Spark) #Data Ingestion #Monitoring #Pandas #REST (Representational State Transfer) #Data Modeling #Agile #Azure #Cloud #MySQL #Scala #SQL Queries #Snowflake #SQL Server #Data Science #Data Engineering #Apache Spark #AWS (Amazon Web Services) #Storage #Azure Data Factory #Data Pipeline #Docker #NoSQL #"ETL (Extract #Transform #Load)" #GIT
Role description
Job Brief
We are looking for an experienced Data Engineer / Python Developer with 4–5 years of hands-on experience in developing data pipelines, backend applications, and scalable data processing solutions. The ideal candidate should have strong expertise in Python, SQL, ETL/ELT, PySpark, cloud platforms, and data engineering frameworks.
Key Responsibilities
• Design, develop, and maintain scalable data pipelines and ETL/ELT workflows using Python and SQL.
• Develop Python-based applications, scripts, APIs, and automation solutions.
• Build and optimize data processing pipelines using PySpark/Apache Spark.
• Work with relational and NoSQL databases for data ingestion, transformation, and storage.
• Implement data quality, validation, monitoring, and error-handling processes.
• Develop and maintain workflows using tools such as Airflow, Azure Data Factory, or Databricks.
• Integrate data from APIs, databases, files, and cloud-based sources.
• Optimize SQL queries and data pipelines for performance and scalability.
• Collaborate with data scientists, analysts, software engineers, and business stakeholders.
• Follow CI/CD, Git, Agile, and data engineering best practices.
Required Skills
• 4–5 years of professional experience in Data Engineering/Python Development.
• Strong proficiency in Python and SQL.
• Hands-on experience with Pandas, NumPy, PySpark, and REST APIs.
• Strong understanding of ETL/ELT, data warehousing, data modeling, and database concepts.
• Experience with Apache Spark and distributed data processing.
• Experience with at least one cloud platform: AWS, Azure, or GCP.
• Knowledge of Databricks, Airflow, Azure Data Factory, or similar orchestration tools.
• Experience with PostgreSQL, MySQL, SQL Server, Snowflake, or similar databases.
• Strong knowledge of Git, CI/CD, testing, and Agile methodologies.
Tools & Technologies
Python, SQL, PySpark, Pandas, NumPy, Apache Spark, Airflow, Databricks, Azure Data Factory/Microsoft Fabric, AWS/Azure/GCP, PostgreSQL, Snowflake, REST APIs, Git, Docker, CI/CD.
Preferred Certifications: If the candidate has any of these certifications, it'll be good
• Microsoft Certified: Fabric Data Engineer Associate (DP-700)
• Databricks Certified Data Engineer Associate
• Google Cloud Professional Data Engineer
• AWS Certified Data Engineer – Associate
Job Brief
We are looking for an experienced Data Engineer / Python Developer with 4–5 years of hands-on experience in developing data pipelines, backend applications, and scalable data processing solutions. The ideal candidate should have strong expertise in Python, SQL, ETL/ELT, PySpark, cloud platforms, and data engineering frameworks.
Key Responsibilities
• Design, develop, and maintain scalable data pipelines and ETL/ELT workflows using Python and SQL.
• Develop Python-based applications, scripts, APIs, and automation solutions.
• Build and optimize data processing pipelines using PySpark/Apache Spark.
• Work with relational and NoSQL databases for data ingestion, transformation, and storage.
• Implement data quality, validation, monitoring, and error-handling processes.
• Develop and maintain workflows using tools such as Airflow, Azure Data Factory, or Databricks.
• Integrate data from APIs, databases, files, and cloud-based sources.
• Optimize SQL queries and data pipelines for performance and scalability.
• Collaborate with data scientists, analysts, software engineers, and business stakeholders.
• Follow CI/CD, Git, Agile, and data engineering best practices.
Required Skills
• 4–5 years of professional experience in Data Engineering/Python Development.
• Strong proficiency in Python and SQL.
• Hands-on experience with Pandas, NumPy, PySpark, and REST APIs.
• Strong understanding of ETL/ELT, data warehousing, data modeling, and database concepts.
• Experience with Apache Spark and distributed data processing.
• Experience with at least one cloud platform: AWS, Azure, or GCP.
• Knowledge of Databricks, Airflow, Azure Data Factory, or similar orchestration tools.
• Experience with PostgreSQL, MySQL, SQL Server, Snowflake, or similar databases.
• Strong knowledge of Git, CI/CD, testing, and Agile methodologies.
Tools & Technologies
Python, SQL, PySpark, Pandas, NumPy, Apache Spark, Airflow, Databricks, Azure Data Factory/Microsoft Fabric, AWS/Azure/GCP, PostgreSQL, Snowflake, REST APIs, Git, Docker, CI/CD.
Preferred Certifications: If the candidate has any of these certifications, it'll be good
• Microsoft Certified: Fabric Data Engineer Associate (DP-700)
• Databricks Certified Data Engineer Associate
• Google Cloud Professional Data Engineer
• AWS Certified Data Engineer – Associate
