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.
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Design and develop ETL pipelines using SSIS, Azure Data Factory, and Databricks
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Build and optimize ClickHouse ingestion pipelines (batch + streaming)
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Develop transformations for structured and semi-structured data
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Optimize SQL Server and ClickHouse queries for performance and scalability
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Improve data models, partitions, and materialized views in ClickHouse
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Integrate data from multiple sources (APIs, SQL Server, cloud storage, Kafka/Event Hubs)
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Monitor pipeline performance and ensure low latency + high reliability
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Implement data quality checks, error handling and lineage tracking
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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:
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ETL tools: Azure Data Factory, SSIS, Databricks
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Strong SQL skills (writing, optimizing, and troubleshooting complex queries)
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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)
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Exposure to big data frameworks
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Understanding of DevOps/Data pipeline deployment practices
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Experience supporting BI tools (Power BI, Tableau)
What Success Looks Like:-
You can independently build and optimize ETL pipelines
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You understand how to make data systems faster, cleaner, and scalable
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You're comfortable working across engineering, analytics, and business teams
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You proactively identify performance issues and fix them
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