Orion Innovation
Azure Data Engineer
Montvale, New Jersey, United StatesmidAdded today
About this role
Orion Innovation seeks an experienced Azure Data Engineer to design, build, and optimize cloud-based data solutions. You'll leverage Azure, Databricks, and SQL expertise to develop ETL pipelines, troubleshoot data issues, and support production systems while mentoring technical teams.
What you'll do
- Design and implement Azure data engineering solutions using Databricks, PySpark, and Python
- Develop and optimize T-SQL queries, stored procedures, and data warehouse architectures
- Troubleshoot ETL pipeline failures, data quality issues, and performance bottlenecks
- Support production data platforms and perform root-cause analysis on incidents
- Mentor junior engineers and communicate technical solutions to stakeholders
- Integrate with optional tools like Azure Data Factory, ADLS, and Power BI for end-to-end data flows
What they're looking for
- Azure data platform (Data Factory, ADLS, Synapse or similar)
- Databricks and PySpark for distributed data processing
- Python programming for data transformations
- Advanced T-SQL, query optimization, and indexing strategies
- ETL pipeline design and troubleshooting
- RDBMS concepts and production support practices
- Incident management and root-cause analysis
- Technical leadership and stakeholder communication
Opens the official application on the employer’s site. No login required.
Orion Innovation
Orion Innovation builds scalable web applications and AI-powered enterprise software solutions, with expertise in cloud-native database systems and GxP-compliant digital systems. The company is hiring full stack developers, sales professionals, database engineers, validation engineers, and support specialists to support its product development and market expansion efforts.
View all jobs at Orion InnovationLikely interview questions
- Walk us through a complex ETL pipeline failure you diagnosed—what was the root cause and how did you resolve it?
- How do you optimize T-SQL queries and execution plans when dealing with large datasets?