Hands-on developer to go job-by-job and procedure-by-procedure through a healthcare payer's Claims batch setting (jobs, stored procedures, Tidal-orchestrated) and turn it into an evidence-backed performance baseline and remediation roadmap. Heavy SQL Server + .NET reading, AI-accelerated review, real production impact in 90 days.

Must-Haves
5+ years production .NET / C# and SQL Server, including stored-procedure-heavy codebases
Hands-on with a batch job scheduler Tidal, Control-M, Autosys, or similar
Can read SQL Server Query Store / DMVs / execution plans to diagnose a slow or failing procedure, not just write current ones
Has actually used AI/LLM tools (Copilot, Claude, ChatGPT) in a real engineering workflow — code review, refactor, or analysis at scale — and knows where to trust it and where not to
Comfortable in ambiguous legacy code: business logic buried in stored procedures, sparse documentation, hundreds of jobs

Nice-to-Haves




Healthcare payer or other regulated, high-volume transactional domain (claims, billing, banking core)
Splunk or equivalent log-based observability tooling
Exposure to config-driven rules engines or event-driven processing patterns
Azure familiarity (not a migration mandate for this role)

What you will Actually Do
Trace Tidal jobs down to the stored procedure and step level; build a job procedure step lineage map
Diagnose bottlenecks and failures using Query Store, DMVs, Tidal run history, Splunk
Use AI to accelerate review of hundreds of stored procedures — every finding personally validated, no blind trust in AI output
Recommend keep / tune / consolidate / retire per job, with evidence
Feed a performance baseline, hotspot inventory, remediation backlog, and 12–18 month roadmap

📌 Net Full Stack Developer Claims Batch Modernization Bengaluru (India)
🏢 Ascendion Engineering
📍 India