Описание

This role involves leading the data engineering team responsible for a GCP-based data platform using BigQuery, dbt, Airflow, and ClickHouse. The Data Engineering Lead will own data pipelines, modelling, reliability, data quality, CI/CD, and stakeholder delivery. The role is remote or hybrid with regular overlap with Gulf Standard Time (UTC+4).

Responsibilities

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
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
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
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
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

Requirements

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
Strong Python, with extensive experience building and debugging production pipelines and services
Advanced SQL, including window functions, complex transformations and query performance analysis
2+ years of production dbt experience: incremental models, macros, tests and documentation
Hands-on experience operating Apache Airflow in production, including backfills, monitoring and troubleshooting
Production experience with BigQuery: data modelling, partitioning, clustering, and query and cost optimisation
Working knowledge of GCP, including Cloud Storage, IAM and cloud data infrastructure
Solid PostgreSQL knowledge: indexing, query performance and incremental extraction
Experience with Apache Flink or a comparable stream or distributed processing framework
Confidence on Linux: Bash, cron, Docker on VMs, log analysis, SSH and basic networking
Strong engineering practice: Git, CI/CD, automated testing and code review

Nice to have

Production experience with ClickHouse, including performance tuning, materialised views and cluster operations
Managed ELT platforms such as Airbyte or Fivetran
CDC and event streaming with Datastream, Kafka or Debezium
Kubernetes and Terraform
BI tools such as Tableau or Power BI, and an understanding of BI performance
LLM APIs, retrieval-augmented generation (RAG) and agent frameworks
Data observability tools such as Soda, Great Expectations or Monte Carlo
AWS services, including EC2, S3 and RDS
Data governance for payments data, such as PCI DSS
Experience in payments, fintech, hospitality technology or a high-growth marketplace

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