ETL Engineer

Job Description

Job Description

Position Summary: 
MedReview is looking for a hands-on ETL Engineer who knows how to build, optimize, and scale data pipelines in a high-performance environment. This is not an entry-level role. You will be working with modern data tools and large datasets, with a strong focus on ClickHouse, SQL performance, and real time data processing. 

If you're someone who can take ownership of data pipeline end-to-end and thrives in a fast-paced data-driven environment, this role is for you.  This is an on-site role Monday - Thursday with remote Fridays. Candidates must be able to consistently work on-site. No exceptions.  Salary $120-130K 

Responsibilities: 
You will be responsible for building and maintaining scalable ETL pipelines that power analytics and business intelligence. 

• Design and develop ETL pipelines using SSIS, Azure Data Factory, and Databricks 
• Build and optimize ClickHouse ingestion pipelines (batch + streaming) 
• Develop transformations for structured and semi-structured data 
• Optimize SQL Server and ClickHouse queries for performance and scalability 
• Improve data models, partitions, and materialized views in ClickHouse
• Integrate data from multiple sources (APIs, SQL Server, cloud storage, Kafka/Event Hubs)
• Monitor pipeline performance and ensure low latency + high reliability 
• Implement data quality checks, error handling and lineage tracking
• Partner with BI teams to support dashboards (Power BI, etc) 

Must-Haves (Non-Negotiables): 
We are targeting candidates who already have strong, hands-on experience in the following: 

• ETL tools: Azure Data Factory, SSIS, Databricks 
• Strong SQL skills (writing, optimizing, and troubleshooting complex queries) 
• Experience working with ClickHouse (schema design,  ingestion, optimization) 
  Experience with cloud environments (Azure perferred) 
• Programing in Python or Scala for data processing 

If you do not have ETL + SQL + ClickHouse exposure, you will not be a fit. 

Nice to Have:

• Experience with streaming data (Kafka, Event Hubs) 
• Exposure to big data frameworks 
• Understanding of DevOps/Data pipeline deployment practices 
• Experience supporting BI tools (Power BI, Tableau) 

What Success Looks Like:

• You can independently build and optimize ETL pipelines 
• You understand how to make data systems faster, cleaner, and scalable 
• You're comfortable working across engineering, analytics, and business teams 
• You proactively identify performance issues and fix them 

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