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.
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
