GitLab · job-boards.greenhouse.io · checked today
Intermediate Data Engineer
Skills, with evidence
- Airflow / orchestration
Experience with orchestration tools such as Airflow, familiarity with dbt, and solid data modeling fundamentals, including Kimball dimensional modeling.
must have - Data modelling
Experience with orchestration tools such as Airflow, familiarity with dbt, and solid data modeling fundamentals, including Kimball dimensional modeling.
must have - Data quality
Use monitoring, service-level agreements (SLAs), data contracts, query and warehouse tuning, and data quality checks to keep the platform reliable and cost-efficient.
must have - Incremental loads
Hands-on experience with ELT or extract, transform, load (ETL) pipelines at scale, including incremental loads, schema changes, backfills, and API ingestion.
must have - Python
Professional experience shipping production data pipelines in Python.
must have - SQL
Deep, hands-on SQL skills and experience with a cloud data warehouse; Snowflake is preferred.
must have - Governance & security
GitLab enables organizations to increase developer productivity, improve operational efficiency, reduce security and compliance risk, and accelerate digital transformation.
not practised here - Warehousing
Use monitoring, service-level agreements (SLAs), data contracts, query and warehouse tuning, and data quality checks to keep the platform reliable and cost-efficient.
- Kubernetes
Manage and maintain the infrastructure behind the platform, including SaaS tools such as Snowflake, Fivetran, and Monte Carlo, and self-managed services such as Airflow and Kubernetes.
not practised here - Failure handling
Use monitoring, service-level agreements (SLAs), data contracts, query and warehouse tuning, and data quality checks to keep the platform reliable and cost-efficient.
- Idempotency & backfills
Hands-on experience with ELT or extract, transform, load (ETL) pipelines at scale, including incremental loads, schema changes, backfills, and API ingestion.
- Schema evolution & contracts
Use monitoring, service-level agreements (SLAs), data contracts, query and warehouse tuning, and data quality checks to keep the platform reliable and cost-efficient.
- dbt
Experience with orchestration tools such as Airflow, familiarity with dbt, and solid data modeling fundamentals, including Kimball dimensional modeling.
- GCP
Docker, Kubernetes, Google Cloud Platform (GCP), or Terraform;
not practised here
Your plan
- SQL
drills at the Mid level
- Python
drills at the Mid level
- Modelling
drills at the Mid level
- Pipelines
drills at the Mid level
The full plan, drill by drill, is on the job page.
leaves in 8 dremoved the moment GitLab closes it