Mit Patel

• Worked on loyalty data flows where the core loyalty platform was operated by a third‑party provider.
• Integrated third‑party loyalty APIs (earn/burn balance checks, accrual/redemption validation) from Java Spring Boot services; handled auth (API keys/OAuth2), idempotent keys, retries, and back-off for transient failures.
• Built validation logic to cross‑check loyalty responses against order context (SKU eligibility, promo windows, partner rules) and raise reconciliation flags.
• Implemented Spark‑based ETL jobs to ingest provider exports (JSON/CSV), normalise schemas, and generate daily reconciliation tables used by analysts.
• Added Python utilities to run data‑quality checks (row counts, schema drift, duplicate transaction IDs) and publish HTML/QC reports.
• Containerised services with Docker and prepared Kubernetes deployment specs (readiness/liveness probes, ConfigMaps/Secrets, resource limits).
• Set up CI/CD (GitHub Actions) for unit/integration tests, static checks, and container builds on each PR.
• Collaborated with product/QA and the vendor team to triage API issues, document edge cases (partial approvals, stale balances), and improve monitoring.

• Built and refined a CPU‑trace–driven simulator that ingests execution traces and replays CPU behaviour to produce timing/throughput metrics and correctness checks.
• Generated input traces using ARM‑based architectures via QEMU, then normalised/validated them before simulation.
• Implemented trace parsing and simulation logic (state machine, event scheduling, counters) and wrote utilities to summarise results for engineers.
• Automated runs on Linux with scripts to batch‑process large traces, compare baselines vs. new builds, and flag regressions.
• Collaborated with senior engineers to interpret results, document edge cases, and improve reproducibility.

Tech exposure: QEMU, ARM architecture concepts, CPU trace formats, Linux, C++, Python (tooling), Git, CI, issue tracking.