Qlub Senior Data Engineer at Qlub responsible for owning and operating the data platform including ingestion, modelling, serving, and reliability. Mentor engineers and set standards for data quality and pipeline management.
Responsibilities
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.
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.
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.
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.
Qualification
You collaborate well with engineers5+ years in data or backend engineeringStrong PythonAdvanced SQL2+ years of production dbt experienceStrong BigQuery skillsGood knowledge of GCPExperience with LLM APIs
Required
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.
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.
Preferred
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.
