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Описание: |
MTicket is building a production AI Runtime on top of a real ticketing and live events business.
We work with data from ticket sales, payments, scanners, live events, venues, artists, marketing, finance, and operations. Before AI agents can automate workflows and support business decisions, this data must be clean, normalized, structured, monitored, and AI-ready.
We are looking for a Senior Data Engineer / Data Architect who can design and own the unified data layer for MTicket AI OS.
What You’ll Do
* Design a unified data model for ticketing, events, payments, scans, artists, venues, marketing, and finance
* Build ETL / ELT pipelines from core systems and external APIs
* Normalize sales, tickets, refunds, scans, payments, event history, and transaction logs
* Create analytics-ready and AI-ready datasets for internal teams and AI agents
* Implement data quality checks, freshness tracking, monitoring, and alerting
* Debug production data issues using logs, metrics, and system behavior
* Work closely with the AI / Python team on datasets for AI agents
Tech Stack
PostgreSQL · Python · SQL · dbt / Airflow / Dagster / Prefect · SQS / Kafka / queues · AWS · OpenTelemetry
Nice to have: ClickHouse / BigQuery / Snowflake / Redshift, pgvector, embeddings
Must Have
* Strong SQL and data modeling skills
* Production experience with PostgreSQL
* ETL / ELT pipeline engineering
* Data warehouse / data layer design experience
* Event-driven or streaming data experience
* API integrations as data sources
* Data quality checks in production
* Pipeline monitoring, alerting, and observability
* Strong debugging skills in production environments
Strong Plus
* Python for data pipelines
* dbt, Airflow, Dagster, or Prefect
* Kafka, SQS, RabbitMQ, or other queues
* ClickHouse, BigQuery, Snowflake, or Redshift
* pgvector, embeddings, or AI-related data infrastructure
* Experience with ticketing, e-commerce, fintech, marketplaces, CRM, or payments data
First 90 Days
* Audit existing MTicket data sources
* Design the first version of the unified data model
* Build initial ETL pipelines for sales, events, and payments
* Add data quality checks and validation rules
* Set up monitoring, alerting, and freshness tracking
* Deliver the first AI-ready datasets to the Python / AI team
Format
* Remote
* Flexible timezone
* Full-time, part-time, or consulting format
* High ownership
* Architect-level impact
* Ukrainian or Russian language is important for collaboration
* English for technical documentation is a plus
You will build the data layer that allows AI agents to understand and operate a real ticketing business.
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