ETL Engineer

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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