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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