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qlub is a fast-scaling fintech backed by Mastercard, Mubadala, e&, and Shorooq. The company has raised $72M and operates across multiple markets globally, with headcount that doubled in the past year. qlub removes friction from restaurant payments: guests scan a QR code and pay in seconds with no app download or waiting. The platform spans Pay-at-Table, Digital Menus, Order-and-Pay, Payment Links, and SoftPOS terminals, enabling restaurants to focus on hospitality while qlub handles the full payment experience.
As Senior Data Engineer, you will own the data platform end-to-end. The current stack runs on GCP with BigQuery as the warehouse, dbt for modeling, Airflow for orchestration, and ClickHouse for low-latency analytics. The platform feeds internal reporting, merchant-facing analytics, and AI-assisted tooling.
This is a hands-on role. Most of your time is spent building and running pipelines, fixing issues, and ensuring data trustworthiness. You will also set standards for the team and mentor junior and mid-level engineers.
Key responsibilities include:
**Ingestion**: Own data ingestion from transactional databases (CDC), SaaS sources, application events, and third-party APIs. Handle backfills, retries, schema changes, and late-arriving data.
**Modeling**: Own the dbt project end-to-end, including staging through marts, tests, documentation, incremental models, and naming conventions. Optimize BigQuery performance through partitioning, clustering, query tuning, and cost monitoring.
**Serving and Infrastructure**: Serve low-latency analytics from ClickHouse with table design, sorting keys, materialized views, and aggregation strategies. Manage the GCP estate including Cloud Storage, Cloud Run, IAM, and external data sources.
**Reliability**: Run production Airflow with scheduling, dependencies, retries, and alerting. Implement freshness and quality checks, fix root causes, and establish CI/CD and testing for data code.
**AI and Team**: Identify where LLMs and agents genuinely help (data discovery, quality monitoring, troubleshooting) and ship solutions. Review code, mentor engineers, and document decisions and runbooks.
**Requirements**:
- 5+ years in data or backend engineering, primarily on production systems
- Strong Python with years of writing and debugging production pipelines and services
- Advanced SQL: window functions, complex transformations, query optimization
- 2+ years of production dbt experience, including incremental models, macros, tests, and documentation
- Experience running production Airflow DAGs with backfills, monitoring, and troubleshooting
- Strong BigQuery skills: modeling, partitioning, clustering, query and cost tuning
- Good GCP knowledge: Cloud Storage, IAM, cloud data infrastructure
- Solid PostgreSQL experience: indexing, query performance, incremental extraction
- Experience with stream or distributed processing (Apache Flink or similar)
- Comfortable on Linux VMs: Bash, cron, Docker, logs, SSH, basic networking
- Strong engineering practices: Git, CI/CD, tests, code review
**Nice to have**: ClickHouse or production OLAP experience, managed ETL/ELT platforms (Airbyte, Fivetran), Datastream for CDC, Kafka and Debezium, Kubernetes and Terraform, BI tools (Tableau, Power BI), LLM APIs and RAG frameworks, data observability tools (Soda, Great Expectations, Monte Carlo), AWS experience, CS or engineering degree.
