Vertisystem
Data Engineer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Senior Data Engineer focused on large-scale data platforms, requiring extensive experience with SQL, Python, Spark, and cloud services (AWS, GCP, Azure). Contract length is unspecified, and pay rate is competitive. W2 candidates only.
🌎 - Country
United States
💱 - Currency
$ USD
-
💰 - Day rate
840
-
🗓️ - Date
July 21, 2026
🕒 - Duration
Unknown
-
🏝️ - Location
Unknown
-
📄 - Contract
W2 Contractor
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🔒 - Security
Unknown
-
📍 - Location detailed
United States
-
🧠 - Skills detailed
#ML (Machine Learning) #GCP (Google Cloud Platform) #Airflow #Data Architecture #Kafka (Apache Kafka) #Scala #Databricks #AWS (Amazon Web Services) #Azure #Oracle #Spark (Apache Spark) #Data Science #Data Engineering #Storage #Monitoring #Python #SQL (Structured Query Language) #Snowflake #Code Reviews #Data Modeling #AI (Artificial Intelligence) #Data Quality #Delta Lake #"ETL (Extract #Transform #Load)" #Data Pipeline #Automation #Data Governance #Cloud
Role description
Senior Data Engineer – Large-Scale Data Platforms
⚠️ W2 ONLY: We are only considering candidates who can work directly on W2. No C2C, 1099, or third-party candidates will be considered.
Summary:
We are seeking an experienced Data Engineer with a strong background building and operating large-scale, cloud-native data platforms in high-growth or enterprise environments. This role is best suited for engineers who have worked with high-volume distributed data systems, scalable ETL/ELT pipelines, and modern data architectures supporting analytics, machine learning, and business-critical applications.
Candidates with experience from leading technology companies or organizations operating at massive scale are highly encouraged to apply.
Key Responsibilities
• Design, build, and maintain highly scalable, fault-tolerant data pipelines and distributed data platforms.
• Develop robust ETL/ELT frameworks for processing large volumes of structured and unstructured data.
• Architect data solutions that support analytics, reporting, AI/ML, and real-time business insights.
• Optimize data models, storage, and processing for performance, reliability, and scalability.
• Partner with Data Scientists, Software Engineers, Product Managers, and Analytics teams to deliver data-driven solutions.
• Improve data quality, governance, monitoring, and operational excellence across enterprise data platforms.
• Build reusable frameworks, automation, and data engineering best practices.
• Drive technical design discussions, code reviews, and continuous improvements.
Preferred Qualifications
• Experience designing and maintaining large-scale distributed data systems.
• Strong expertise with SQL, Python, Spark, and modern ETL/ELT frameworks.
• Experience with cloud platforms such as AWS, GCP, or Azure.
• Hands-on experience with technologies such as Databricks, Snowflake, Kafka, Airflow, Hive, Delta Lake, or similar modern data platforms.
• Strong understanding of data modeling, data warehousing, orchestration, and data governance.
• Excellent problem-solving, communication, and cross-functional collaboration skills.
• Experience supporting data platforms used for analytics, machine learning, or large-scale business operations.
Ideal Background
This opportunity is ideal for engineers who have built and maintained enterprise-scale data platforms at organizations such as Meta, Google, Amazon, Microsoft, Apple, Netflix, Uber, Airbnb, LinkedIn, Salesforce, Adobe, PayPal, Oracle, NVIDIA, Cisco, or other large technology-driven organizations.
Senior Data Engineer – Large-Scale Data Platforms
⚠️ W2 ONLY: We are only considering candidates who can work directly on W2. No C2C, 1099, or third-party candidates will be considered.
Summary:
We are seeking an experienced Data Engineer with a strong background building and operating large-scale, cloud-native data platforms in high-growth or enterprise environments. This role is best suited for engineers who have worked with high-volume distributed data systems, scalable ETL/ELT pipelines, and modern data architectures supporting analytics, machine learning, and business-critical applications.
Candidates with experience from leading technology companies or organizations operating at massive scale are highly encouraged to apply.
Key Responsibilities
• Design, build, and maintain highly scalable, fault-tolerant data pipelines and distributed data platforms.
• Develop robust ETL/ELT frameworks for processing large volumes of structured and unstructured data.
• Architect data solutions that support analytics, reporting, AI/ML, and real-time business insights.
• Optimize data models, storage, and processing for performance, reliability, and scalability.
• Partner with Data Scientists, Software Engineers, Product Managers, and Analytics teams to deliver data-driven solutions.
• Improve data quality, governance, monitoring, and operational excellence across enterprise data platforms.
• Build reusable frameworks, automation, and data engineering best practices.
• Drive technical design discussions, code reviews, and continuous improvements.
Preferred Qualifications
• Experience designing and maintaining large-scale distributed data systems.
• Strong expertise with SQL, Python, Spark, and modern ETL/ELT frameworks.
• Experience with cloud platforms such as AWS, GCP, or Azure.
• Hands-on experience with technologies such as Databricks, Snowflake, Kafka, Airflow, Hive, Delta Lake, or similar modern data platforms.
• Strong understanding of data modeling, data warehousing, orchestration, and data governance.
• Excellent problem-solving, communication, and cross-functional collaboration skills.
• Experience supporting data platforms used for analytics, machine learning, or large-scale business operations.
Ideal Background
This opportunity is ideal for engineers who have built and maintained enterprise-scale data platforms at organizations such as Meta, Google, Amazon, Microsoft, Apple, Netflix, Uber, Airbnb, LinkedIn, Salesforce, Adobe, PayPal, Oracle, NVIDIA, Cisco, or other large technology-driven organizations.
