Senior Data Platform Engineer - Spark, SQL & Cloud

Ethereum Technologies LLC; Sunnyvale (CA); On-site; USD 140,000 - 170,000; Full time

Job summary

ETHEREUM TECHNOLOGIES LLC is seeking a Back-End Engineer with a strong data engineering background to design, develop, and maintain scalable data pipelines and back-end systems in a cloud environment. You will work with large datasets, collaborate with data scientists, and ensure data quality and security across the stack.

The role involves building real-time streaming pipelines with MSK/Kafka, leveraging S3 and Iceberg for data lakes, and writing optimized SQL for ETL workloads.

Qualifications

• Bachelor’s degree in computer science or related field or equivalent experience.
• 5+ years of experience in back-end development with a focus on data engineering.

Responsibilities

• Design, develop, and maintain data pipelines using Spark, Python, Scala, and Java.
• Write efficient SQL queries for ETL processes.
• Work with DataFrames to manipulate and analyze large datasets.
• Implement data storage and processing solutions using cloud technologies (AWS or GCP).
• Build real-time data streaming pipelines using MSK/Kafka.
• Utilize S3 for data storage and retrieval and work with Iceberg for data lakes.
• Ensure data quality, integrity, and security; collaborate with data scientists and engineers.
• Participate in code reviews and improve development processes.
• Troubleshoot and resolve issues in data pipelines and back-end systems.

Skills

Spark

Python

Scala

Java

SQL

DataFrames

AWS

Kafka

S3

Iceberg

Education

Bachelor’s degree in Computer Science or related field

Tools

AWS

GCP

MSK

Kafka

S3

Iceberg

Job description

ETHEREUM TECHNOLOGIES LLC is seeking a Back-End Engineer with a strong data engineering background to design, develop, and maintain scalable data pipelines and back-end systems in a cloud environment. You will work with large datasets, collaborate with data scientists, and ensure data quality and security across the stack.

The role involves building real-time streaming pipelines with MSK/Kafka, leveraging S3 and Iceberg for data lakes, and writing optimized SQL for ETL workloads.