Data Engineer

Jibe Ventures; Amsterdam; On-site; EUR 70,000 - 110,000; Full time

Job summary

Qogita is looking for a data engineer to build and maintain reliable ELT pipelines and ensure trusted, queryable datasets across the platform. You will collaborate with Analytics, Product, Science, and Engineering to keep the data warehouse accurate and accessible.

You’ll own the dbt modeling layer, monitor pipeline health, and optimize schemas while supporting cross-functional reporting needs. Join a fast-moving team focused on reliable data platforms.

Qualifications

• 3+ years of experience building production data pipelines with a modern ELT stack.
• Hands-on experience with dbt for data modeling and transformation.
• Strong SQL and Python skills for pipeline development and data validation.
• Experience with cloud data warehouses (Snowflake, BigQuery, or Redshift).
• Familiarity with orchestration tools such as Airflow or Dagster.
• Able to work independently in a fast-moving scale-up environment with evolving priorities.

Responsibilities

• Build and maintain ELT pipelines that move data from production systems into the data warehouse.
• Manage the modeling layer in dbt, ensuring transformations are tested, documented, and performant at scale.
• Deliver new datasets and metrics in partnership with Analytics and Product to support cross-functional reporting needs.
• Own the monitoring and alerting for pipeline failures and data quality issues, resolving incidents before they reach downstream consumers.
• Analyse query performance and warehouse costs, and improve schema design and partitioning strategy accordingly.
• Develop internal tooling and documentation that make the data platform easier for other engineers to use.
• Work as part of the platform team, partnering with other platform engineers to keep the data infrastructure reliable and well-integrated with the broader systems other teams build on.

Skills

ELT pipelines

dbt

SQL

Python

Snowflake

BigQuery

Redshift

Airflow

Dagster

Independence

Job description

You're a data engineer who builds and maintains reliable pipelines that turn raw commercial and operational data into trusted, queryable datasets. You bring hands-on experience with modern ELT tooling and a track record of shipping pipelines that hold up under production load. This role exists to make sure every team at Qogita can trust the data they build on.
The Data Engineering team owns the ingestion, transformation, and delivery of data across Qogita's platform, working closely with Analytics, Product, Science, and Engineering to keep the data warehouse accurate and available.

• Build and maintain ELT pipelines that move data from Qogita's production systems into the data warehouse.
• Manage the modeling layer in dbt, ensuring transformations are tested, documented, and performant at scale.
• Deliver new datasets and metrics in partnership with Analytics and Product to support cross-functional reporting needs.
• Own the monitoring and alerting for pipeline failures and data quality issues, resolving incidents before they reach downstream consumers.
• Analyse query performance and warehouse costs, and improve schema design and partitioning strategy accordingly.
• Develop internal tooling and documentation that make the data platform easier for other engineers to use.
• Work as part of the platform team, partnering with other platform engineers to keep the data infrastructure reliable and well-integrated with the broader systems other teams build on.

• 3+ years of experience building production data pipelines with a modern ELT stack.
• Hands-on experience with dbt for data modeling and transformation.
• Working knowledge of SQL and Python, applied to pipeline development and data validation.
• Experience with a cloud data warehouse such as Snowflake, BigQuery, or Redshift.
• Familiarity with orchestration tools such as Airflow or Dagster.
• Able to work independently with minimal guidance and identify data quality issues before they affect downstream teams.
• Comfortable operating in a fast-moving scale-up environment with evolving priorities.