Niktor Inc
Data Engineer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Data Engineer with 4-8 years of experience, offering a remote contract in the U.S. at a competitive pay rate. Key skills include SQL, Python, Snowflake, and cloud platforms. Certifications in AWS, Azure, or GCP are preferred.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
Unknown
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🗓️ - Date
October 24, 2025
🕒 - Duration
Unknown
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🏝️ - Location
Remote
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📄 - Contract
Unknown
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🔒 - Security
Unknown
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📍 - Location detailed
United States
-
🧠 - Skills detailed
#Data Warehouse #Data Engineering #Compliance #ML (Machine Learning) #Data Modeling #dbt (data build tool) #Apache Spark #Data Governance #AWS (Amazon Web Services) #Infrastructure as Code (IaC) #Kubernetes #Azure #Terraform #GCP (Google Cloud Platform) #Delta Lake #Data Science #Snowflake #Databricks #Data Integrity #Kafka (Apache Kafka) #Scala #Data Analysis #Python #Schema Design #Spark (Apache Spark) #Data Quality #Security #SQL (Structured Query Language) #Version Control #"ETL (Extract #Transform #Load)" #Azure Data Factory #GitHub #Cloud #GIT #ADF (Azure Data Factory) #Docker #Redshift #Documentation #Airflow #Data Pipeline #GDPR (General Data Protection Regulation)
Role description
Job Title: Data Engineer
Location: Remote (United States)
Experience: 4 – 8 Years
About the Role:
We are seeking a highly skilled Data Engineer to design, develop, and optimize data pipelines and architectures that enable advanced analytics and real-time data insights. The ideal candidate should be passionate about data quality, scalability, and performance—and bring innovative solutions to complex data challenges.
Key Responsibilities:
• Design, build, and maintain scalable ETL/ELT pipelines for structured and unstructured data using modern tools and cloud platforms.
• Develop and manage data warehouse and lakehouse solutions using Snowflake, Redshift, or Databricks.
• Implement Data Mesh architecture principles to decentralize data ownership and empower domain-based data teams.
• Optimize data models for analytics and reporting, ensuring data integrity, lineage, and performance.
• Work closely with data analysts, data scientists, and product teams to support data-driven decision-making.
• Integrate streaming data using Kafka, Spark Streaming, or Kinesis for near-real-time insights.
• Automate workflows and CI/CD for data pipelines using Airflow, GitHub Actions, or Azure Data Factory.
• Apply DBT (Data Build Tool) best practices for transformations, documentation, and testing.
• Ensure compliance with data governance, security, and privacy policies (GDPR, HIPAA, etc.).
Required Skills:
• 4–8 years of experience as a Data Engineer or in a similar data-intensive role.
• Strong expertise in SQL, Python, and at least one cloud platform (AWS / Azure / GCP).
• Proven experience with Snowflake and DBT (with performance optimization experience).
• Hands-on experience with Apache Spark, Kafka, or Flink.
• Solid understanding of data modeling, data warehousing, and schema design.
• Familiarity with containerization (Docker/Kubernetes) and version control (Git).
• Exposure to Data Mesh Architecture or domain-oriented data ownership models (rare but preferred).
• Excellent communication, problem-solving, and analytical thinking skills.
Preferred Qualifications:
• Experience with Delta Lake or Iceberg tables.
• Familiarity with Terraform or CloudFormation for infrastructure as code.
• Knowledge of Machine Learning data pipelines or feature store design.
• Certification in AWS Data Analytics, Azure Data Engineer, or GCP Professional Data Engineer.
Job Title: Data Engineer
Location: Remote (United States)
Experience: 4 – 8 Years
About the Role:
We are seeking a highly skilled Data Engineer to design, develop, and optimize data pipelines and architectures that enable advanced analytics and real-time data insights. The ideal candidate should be passionate about data quality, scalability, and performance—and bring innovative solutions to complex data challenges.
Key Responsibilities:
• Design, build, and maintain scalable ETL/ELT pipelines for structured and unstructured data using modern tools and cloud platforms.
• Develop and manage data warehouse and lakehouse solutions using Snowflake, Redshift, or Databricks.
• Implement Data Mesh architecture principles to decentralize data ownership and empower domain-based data teams.
• Optimize data models for analytics and reporting, ensuring data integrity, lineage, and performance.
• Work closely with data analysts, data scientists, and product teams to support data-driven decision-making.
• Integrate streaming data using Kafka, Spark Streaming, or Kinesis for near-real-time insights.
• Automate workflows and CI/CD for data pipelines using Airflow, GitHub Actions, or Azure Data Factory.
• Apply DBT (Data Build Tool) best practices for transformations, documentation, and testing.
• Ensure compliance with data governance, security, and privacy policies (GDPR, HIPAA, etc.).
Required Skills:
• 4–8 years of experience as a Data Engineer or in a similar data-intensive role.
• Strong expertise in SQL, Python, and at least one cloud platform (AWS / Azure / GCP).
• Proven experience with Snowflake and DBT (with performance optimization experience).
• Hands-on experience with Apache Spark, Kafka, or Flink.
• Solid understanding of data modeling, data warehousing, and schema design.
• Familiarity with containerization (Docker/Kubernetes) and version control (Git).
• Exposure to Data Mesh Architecture or domain-oriented data ownership models (rare but preferred).
• Excellent communication, problem-solving, and analytical thinking skills.
Preferred Qualifications:
• Experience with Delta Lake or Iceberg tables.
• Familiarity with Terraform or CloudFormation for infrastructure as code.
• Knowledge of Machine Learning data pipelines or feature store design.
• Certification in AWS Data Analytics, Azure Data Engineer, or GCP Professional Data Engineer.
