Why qlub
The end of a great meal shouldn't be the slow part. Waiting for the check, finding someone to take payment, splitting a bill by hand. qlub removes that friction. Guests scan a QR code and pay in seconds. No app, no download, no waiting.
From Pay-at-Table and Digital Menus to Order-and-Pay, Payment Links, and SoftPOS terminals, we handle the full payment experience so restaurants can focus on what they do best: great hospitality. Less friction means faster table turnover, smoother operations, and higher profit.
We're a fast-scaling fintech backed by Mastercard, Mubadala, e& and Shorooq. $72M raised, live across multiple markets worldwide, and headcount doubled in the past year. We're building the teams that take us to the next stage.
The role
We need a Senior Data Engineer to own our data platform. Today it runs on GCP: BigQuery as the warehouse, dbt for modelling, Airflow for orchestration, and ClickHouse where we need low-latency analytics. It feeds internal reporting, merchant-facing analytics, and a growing set of AI-assisted tooling.
This is a hands-on job. Most of your week is building and running pipelines, fixing what breaks, and making the data trustworthy enough that people stop double-checking it. You will also set the standards the rest of the team follows and mentor the engineers around you.
You do not need to have used every tool listed below. You do need to have run production data systems and know what goes wrong with them.
What you’ll own
Ingestion
Own how data gets in: CDC from our transactional databases, SaaS sources, application events, and third-party APIs.
Handle the unglamorous parts well: backfills, retries, schema changes, late-arriving data.
Modelling
Own the dbt project end to end: staging through marts, tests, docs, incremental models, and the naming and contract conventions everyone follows.
Keep BigQuery fast and cheap: partitioning, clustering, query tuning, and cost monitoring.
Serving and infrastructure
Serve low-latency analytics from an OLAP store such as ClickHouse: table design, sorting keys, materialized views, aggregation strategy.
Look after the GCP estate around the warehouse: Cloud Storage, Cloud Run services, IAM, and the occasional read from sources hosted elsewhere.
Reliability
Run production Airflow: scheduling, dependencies, retries, alerting, and the operational habits that go with it.
Put freshness and quality checks in place, and fix root causes when they fire rather than restarting the job.
Set up CI/CD and tests for data code so changes ship safely.
AI and the team
Find where LLMs and agents genuinely help (data discovery, quality monitoring, troubleshooting), and ship those. Skip the demos.
Review code, mentor junior and mid-level engineers, and write down the decisions and runbooks the team will need later.
Who you are
You own outcomes and you see your work through to production.
You make good decisions quickly with incomplete information.
You use AI to move faster without giving up security, maintainability or engineering judgement.
You're comfortable working anywhere in the stack, and you pick up new tools and languages quickly.
You collaborate well with engineers, product, design and operations teams.
What you’ll need
5+ years in data or backend engineering, most of it on production systems.
Strong Python, with years of writing and debugging production pipelines and services.
Advanced SQL: window functions, complex transformations, and knowing why a query is slow.
2+ years of production dbt experience, including incremental models, macros, tests and docs.
Experience running production Airflow DAGs, including backfills, monitoring and troubleshooting when things break.
Strong BigQuery skills: modelling, partitioning, clustering, and query and cost tuning.
Good knowledge of GCP, including Cloud Storage, IAM and general cloud data infrastructure.
Solid PostgreSQL experience: indexing, query performance and incremental extraction.
Experience with stream or distributed processing, such as Apache Flink or similar.
Comfortable working on a Linux VM: Bash, cron, Docker, reading logs, SSH and basic networking.
Strong engineering practice with Git, CI/CD, tests and code review, writing code other people can maintain.
Nice to have
Experience with ClickHouse or production OLAP: tuning, materialized views and cluster operations.
Experience with managed ETL/ELT platforms such as Airbyte or Fivetran.
Experience with Datastream for CDC and database replication.
Experience with Kafka and Debezium for event streaming and change data capture.
Experience with Kubernetes and Terraform.
Experience with BI tools such as Tableau or Power BI, and a feel for what makes dashboards slow.
Experience with LLM APIs, RAG and agent frameworks.
Experience with data observability tools such as Soda, Great Expectations or Monte Carlo.
Experience with AWS (EC2, S3, RDS).
A CS or engineering degree, though a strong track record matters more.
What we offer
• Real ownership over products that impact millions of diners and thousands of restaurants globally
• The opportunity to solve challenging problems in payments, hospitality, and customer experience
• A fast-moving environment where great ideas quickly become reality
• Close collaboration with experienced operators, builders, and industry experts
• A chance to leave a visible mark on one of the fastest-growing restaurant technology companies globally
Ready to build? Join us and help shape how the world pays at the table. If that excites you, apply now.