Orion Innovation
Azure Data Engineer
- Confirmed live in the last 24 hours
- No salary listed
- Mid level
- On-site · Montvale, New Jersey, United States
- 5+ yrs exp
- Added 1 month ago
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.
- Industry
- Technology & Software
Likely 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?