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 Data Engineering Lead to run our data platform and lead the engineers who build it. It runs on GCP (BigQuery, dbt, Airflow, ClickHouse) and feeds internal reporting, merchant analytics and AI tooling. As a player-coach, you stay hands-on with pipelines, reviews and production fixes while leading the team. Based in Dubai, India or Sri Lanka, remote or hybrid, with regular overlap with Gulf Standard Time (UTC+4).

What you’ll own

Technical leadership

• Lead the team’s day-to-day delivery: plan and prioritise work, hold regular 1:1s and remove blockers.

• Define and uphold engineering standards for code review, testing, naming conventions, data contracts, documentation and on-call.

• Mentor engineers through pairing, design reviews and timely, constructive feedback.

Architecture and platform direction

• Make and document architectural decisions across ingestion, transformation, serving and orchestration.

• Balance new feature work against reliability, scalability and technical debt, and keep the platform roadmap realistic.

• Manage the GCP environment around the warehouse, including Cloud Storage, Cloud Run, IAM and integrations with external cloud-hosted sources.

Data pipelines and modelling

• Own ingestion from transactional databases, SaaS platforms, application events and third-party APIs, including CDC, backfills, schema evolution and late-arriving data.

• Own the dbt project end to end: staging, intermediate and mart layers, incremental models, tests, documentation and modelling conventions.

• Optimise BigQuery for performance and cost through partitioning, clustering and query tuning, with spend tracked against a clear budget.

• Design low-latency analytical serving in ClickHouse or a comparable OLAP store, covering table design, sorting keys, materialised views and aggregation strategy.

Reliability and data quality

• Operate production Airflow workloads with sound scheduling, dependency management, retries and alerting.

• Establish freshness, completeness and quality checks, and resolve issues at the root cause rather than through repeated manual fixes.

• Lead incident response and post-incident reviews, and turn the findings into lasting improvements.

• Build CI/CD and automated testing for data code so that changes reach production safely.

Stakeholders and AI

• Partner with product, analytics, finance and operations to turn business needs into a prioritised delivery plan, and challenge requests where the value is unclear.

• Work with security and compliance on access control, PII handling and data governance.

• Identify practical uses of LLMs and AI agents in data discovery, quality monitoring and troubleshooting, and take the proven ones into production.

Who you are

• You own outcomes, yours and your team's, and decide well without full information

• You keep things simple, know what decisions cost, and explain the trade-offs

• You use AI to move faster without giving up security, quality or judgement

• You grow people, and you communicate clearly across teams and time zones

What you’ll need

• 6+ years in data or backend engineering, predominantly on production systems.

• At least 1 year leading or mentoring data engineers, as a tech lead, team lead or senior engineer with clear technical ownership.

• A track record of owning a data platform, or a significant part of one, across architecture, reliability and cost.