IT services firm specializing in enterprise modernization and healthcare data integration
Siritech is a Microsoft-centric services shop (Azure, .NET, SQL Server) founded in 2023, now executing a deliberate cloud and data-stack migration: adopting Talend, Snowflake, and healthcare standards (FHIR/HL7) while replacing SAS. The hiring pattern — 28 engineering roles and 19 data roles, almost entirely senior-level — signals execution-heavy demand for modernization projects, not early-stage product building. Active work spans legacy ETL-to-cloud conversions, enterprise data warehouse overhauls, and healthcare application rewrites.
Siritech delivers application development, business intelligence, enterprise resource planning, and infrastructure optimization to mid-market and enterprise clients. The firm operates primarily in the United States and maintains a services-first operational model, with deep expertise in web and mobile development, QA testing, data engineering, and application maintenance. Core technical delivery runs on the Microsoft stack (Azure, .NET, SQL Server) with active projects modernizing legacy systems — particularly in healthcare and financial services — toward cloud-native architectures. The company was founded in 2023.
Primary: Azure, .NET, C#, ASP.NET Core, SQL Server, Jira, Azure DevOps. Data layer: Kafka, Entity Framework, Azure Cosmos DB. Adopting: Talend, Snowflake, Informatica. Replacing: SAS.
Enterprise data warehouse modernization, SAS-to-Snowflake conversion, Talend-based ETL pipelines, change data capture (CDC) logic, financial and healthcare system modernization, interactive dashboards, and legacy reporting system upgrades.
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SIRITECH SOLUTIONS CORP's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
This is not an official vendor or customer list. It is a technology-adoption signal inferred from public data, intended for B2B research.