Q

qlub

QR-driven ordering and payments for restaurants

Data Engineering Lead

Engineering LeadershipUpdated on Nov 5
Full-timeNo salary listed

About the Job

Requirements

  • At least 6 years of experience in data or backend engineering, predominantly on production systems.
  • At least 1 year of experience leading or mentoring data engineers as a technical 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.
  • Strong Python skills and extensive experience building and debugging production pipelines and services.
  • Advanced SQL skills, including window functions, complex transformations, and query performance analysis.
  • At least 2 years of production dbt experience, including incremental models, macros, tests, and documentation.
  • Hands-on experience operating Apache Airflow in production, including backfills, monitoring, and troubleshooting.
  • Production experience with BigQuery, including data modelling, partitioning, clustering, and query and cost optimisation.
  • Working knowledge of Google Cloud Platform, including Cloud Storage, IAM, and cloud data infrastructure.
  • Solid PostgreSQL knowledge, including indexing, query performance, and incremental extraction.
  • Experience with Apache Flink or a comparable stream or distributed processing framework.
  • Confidence using Linux, including Bash, cron, Docker on virtual machines, log analysis, SSH, and basic networking.
  • Strong engineering practices, including Git, CI/CD, automated testing, and code review.

Responsibilities

  • Lead the team's day-to-day delivery by planning and prioritising work, holding regular one-to-ones, and removing 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.
  • Make and document architectural decisions across ingestion, transformation, serving, and orchestration.
  • Balance new feature work against reliability, scalability, and technical debt, and maintain a realistic platform roadmap.
  • Manage the Google Cloud Platform environment around the warehouse, including Cloud Storage, Cloud Run, IAM, and integrations with external cloud-hosted sources.
  • Own ingestion from transactional databases, SaaS platforms, application events, and third-party APIs, including change data capture, backfills, schema evolution, and late-arriving data.
  • Own the dbt project end to end, including staging, intermediate and mart layers, incremental models, tests, documentation, and modelling conventions.
  • Optimise BigQuery performance and cost through partitioning, clustering, and query tuning, and track spending against a clear budget.
  • Design low-latency analytical serving in ClickHouse or a comparable online analytical processing store, covering table design, sorting keys, materialised views, and aggregation strategy.
  • Operate production Airflow workloads with sound scheduling, dependency management, retries, and alerting.
  • Establish freshness, completeness, and quality checks, and resolve issues at their root cause rather than through repeated manual fixes.
  • Lead incident response and post-incident reviews, and turn findings into lasting improvements.
  • Build CI/CD and automated testing for data code so changes reach production safely.
  • 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, personally identifiable information handling, and data governance.
  • Identify practical uses of large language models and AI agents in data discovery, quality monitoring, and troubleshooting, and take proven uses into production.

Desired Qualifications

  • Production experience with ClickHouse, including performance tuning, materialised views, and cluster operations.
  • Experience with managed ELT platforms such as Airbyte or Fivetran.
  • Experience with change data capture and event streaming using Datastream, Kafka, or Debezium.
  • Experience with Kubernetes and Terraform.
  • Experience with business intelligence tools such as Tableau or Power BI, and an understanding of business intelligence performance.
  • Experience with large language model APIs, retrieval-augmented generation, and agent frameworks.
  • Experience with data observability tools such as Soda, Great Expectations, or Monte Carlo.
  • Experience with AWS services, including EC2, S3, and RDS.
  • Experience with payments data governance, such as PCI DSS.
  • Experience in payments, fintech, hospitality technology, or a high-growth marketplace.

About the Company

qlub

QR-driven ordering and payments for restaurants

Qlub.io provides a digital ordering and payment platform for restaurants using QR codes. Guests scan a table QR to view menus, place orders, and pay, with options to split bills and add tips. The system supports Apple Pay, Google Pay, and various cards, and integrates with leading payment providers and top POS systems. Restaurants can go live in under 48 hours, and the platform is typically sold via subscription or transaction fees. Qlub.io differentiates itself through strong POS integration, rapid deployment, and features that encourage online reviews, aiming to save time (up to 16 minutes per table) and streamline operations. The company’s goal is to improve customer experience and operational efficiency for restaurants while boosting their online reputation.

Company size
201-500
Company stage
Early VC
Total funding
$72M
Headquarters
Dubai, United Arab Emirates
Founded
2021

Recently Posted Jobs

Explore more opportunities at qlub

See more jobs