Senior Data Engineer

About GMI Cloud

GMI Cloud is building AI infrastructure for the next generation of AI companies, providing an integrated platform across
GPU compute, model inference, and agentic workloads
. We work with AI developers, startups, enterprises, model companies, and technology partners globally as we continue expanding across the U.S. and Asia.

GMI Cloud is looking for a Senior Data Engineer to own the data flows behind our AI inference platform. Every request served on the platform produces usage, billing and monitoring data; this role makes sure those numbers are accurate, reconcilable and traceable from the raw event all the way to the invoice and the revenue report.

You will be the technical owner of the data platform: you design the pipelines, define the data models, set the quality bar, and keep long-range queries fast as volume grows. You will work closely with the Inference Engine (IE) backend team, the frontend and portal teams that surface usage and billing to customers, and the Finance and Operations teams that depend on the numbers.

Key Responsibilities

• Build and run high-volume pipelines that collect usage, billing and monitoring events from the inference platform.
• Aggregate raw events into billable usage and produce consistent datasets for invoicing, revenue reporting and customer-facing views.
• Automate reconciliation across events, usage, billing and finance reports, keeping every number traceable to its source.
• Design BigQuery and PostgreSQL models, partitioning and archiving so long-range queries stay fast and cost-effective.
• Implement data quality checks, alerting and SLOs for data freshness and correctness.
• Run pipelines on GCP and Kubernetes, and work with backend, frontend and Finance teams on reporting needs.

Minimum Qualifications

• 5+ years in data engineering, owning production pipelines end to end.
• Strong SQL on PostgreSQL and solid data modeling (fact/dimension, event and ledger-style models).
• Proficient in at least one backend language, Go preferred.
• Hands-on with BigQuery, GCP and Kubernetes.
• Experience with batch and streaming processing, including idempotency, deduplication and late-arriving data.
• Experience with data quality, reconciliation and monitoring, and clear communication with non-engineering stakeholders.

Nice to Have

• Experience with billing or payments data: metering, rating, invoicing, credits and refunds, revenue recognition inputs, or reconciliation against a payment provider.
• Experience on cloud or AI inference platforms: GPU workloads, token- or time-based usage metering, model-serving metrics, and capacity or cost reporting.
• Experience with orchestration and transformation tools (for example Airflow, dbt) and message systems (for example Kafka or Pub/Sub).
• Experience building audit trails, data lineage or compliance-grade archiving.