Position: Application Integration Developer
Location: REMOTE ROLE
Duration: Full Time Role
Job description
Day to Day:
- Think of yourself as the architect and builder of the company's data highway system. Your job is to design how information flows throughout the organization – from where it's created, to where it's stored, to where people need to use it.
- This role is for someone who can both design the big picture (enterprise data architecture) and roll up their sleeves to build it (hands-on engineering with C#, Azure, SQL, and modern data platforms).
Must Haves:
- 7+ years of experience in application integration or data integration developer experience looking to grow into a data architect role.
- Expertise in backend development with proficiency in enterprise application integration challenges and data pipelines
- Strong expertise in C#/.NET development and cloud-native architecture (Azure strongly preferred)
· You need to be a strong coder in C#/.NET to build custom data applications, integration tools, and backend services that power data pipelines.
· Deep hands-on experience with Microsoft Azure – specifically around data services like Azure Data Factory, Azure SQL, Synapse, and Event Hubs. You should know how to architect and deploy cloud-native data solutions.
· Deep knowledge of SQL and NoSQL database technologies with demonstrated performance optimization capabilities
· Proven experience designing application integration solutions including ETL, API patterns, streaming, and event-drive
· Expert-level SQL skills are non-negotiable. You'll write complex queries, optimize database performance, design schemas, and troubleshoot data issues across both SQL and NoSQL databases.
- Proven ability to design and build enterprise-scale data integration solutions – whether that's traditional ETL (Extract, Transform, Load), real-time streaming with Kafka/Event Hubs, or API-based integrations.
- Experience with at least one major platform like Databricks, Snowflake, Microsoft Fabric, or Synapse. You should understand how to architect data warehouses and lakehouses that scale, perform well, and support analytics.
