Data engineer

Ethereum Technologies; Quincy (MA); On-site; USD 77,000 - 110,000; Full time

Job summary

Ethereum Technologies in Quincy, MA seeks a Data Engineer to develop and optimize Spark jobs and data pipelines using Azure Databricks. You will design lakehouse solutions and implement streaming data transformations.

Candidates should have strong experience with Databricks, Spark SQL, and DevOps integration; Docker/Kubernetes experience is a plus. This is a hands-on, fast-paced role supporting live data workloads.

Qualifications

• Apache Spark and Spark SQL expertise for large-scale data processing.
• ETL/ELT development and data pipeline orchestration.
• Scala and PySpark development with SQL performance tuning.

Responsibilities

• Develop and optimize Spark jobs, data transformations, and processing workflows.
• Implement streaming data solutions using Azure Databricks.
• Design and implement data warehouse/lakehouse solutions.
• Monitor, debug, and resolve issues in live Databricks jobs.
• Troubleshoot DevOps pipelines, Azure, and AKS services.
• Build data ingestion, streaming, and transformation solutions.

Skills

Spark & SQL

ETL pipelines

Scala / PySpark

Azure Databricks

Databricks expertise

Unity Catalog

Auto Loader

Lakehouse architecture

Databricks Serverless

Dataset & DataFrame

Cluster sizing

Job orchestration

Structured Streaming

Tools

Docker

Kubernetes

Job description

US Citizen, Green Card, H-1B, OPT-EAD, CPT, Other

Job Title: Data engineer

Location: Quincy, MA

Duration: 14 Months

Bill Rate: $68/hour (C2C)

Job Type: W-2, 1099, C2C

Client: To Be Discussed Later

Work Authorization: US Citizen, Green Card, OPT-EAD, CPT, H-1B, H4-EAD, Other

Job Description

Mandatory Technical Skills

• Apache Spark, Spark DataFrames, Notebooks and Spark SQL
• ETL/ELT and data pipeline development
• Scala/PySpark and SQL performance tuning
• Azure Databricks and large-volume data processing
• Databricks Expertise
• Unity Catalog
• Auto Loader
• Lakehouse and Medallion Architecture
• Databricks Serverless Options
• Dataset and DataFrame
• Cluster Configuration and Sizing
• Databricks Job Orchestration
• Spark Structured Streaming

Additional Responsibilities & Requirements

• Develop and optimize Spark jobs, data transformations, and processing workflows.
• Implement complex data streaming and transformation solutions using Azure Databricks.
• Design and implement data warehouse/lakehouse solutions.
• Monitor, debug, and resolve issues in live Databricks jobs.
• Troubleshoot DevOps pipelines, Azure, and AKS services.
• Build data ingestion, streaming, and transformation solutions.
• Experience with Apache Big Data technologies and languages such as PySpark, R, or Java is advantageous.
• Docker and Kubernetes experience is good to have.

We are an equal opportunity employer. All aspects of employment including the decision to hire, promote, discipline, or discharge, will be based on merit, competence, performance, and business needs. We do not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical condition, pregnancy, genetic information, gender, sexual orientation, gender identity or expression, national origin, citizenship/ immigration status, veteran status, or any other status protected under federal, state, or local law.