Описание

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.
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.
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.
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.
Find where LLMs and agents genuinely help (data discovery, quality monitoring, troubleshooting), and ship those.
Review code, mentor junior and mid-level engineers, and write down the decisions and runbooks the team will need later.

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.

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.

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

Senior Data Engineer