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.