This is a foundational analytics engineering position within a data platform team that sits at the center of the business. The work involves building and maintaining the data infrastructure pipelines and self-service tooling that analysts data scientists and business stakeholders rely on daily. The team is building out its analytics engineering function from the ground up which means real ownership and direct influence over how data flows across the organization.
This role is based in Barcelona Spain. Relocation assistance is available. The working model is hybrid with two days per week in the office.
What the Work Looks Like Day to Day
Design build and maintain reliable tested and well documented ETL and ELT pipelines and DBT models
Work with stakeholders across the business to understand data needs and translate them into high quality reusable data products
Identify gaps and improvement areas across the data ecosystem and shape the analytics engineering roadmap
Build and maintain self-service tools and datasets that allow engineers data scientists and analysts to get to insights quickly
Partner with Product Engineering Data Science and Analytics teams to enable effective use of the data platform
Investigate and resolve data related bugs and technical issues in platform tooling and DBT models
Contribute to and lead cross-team data projects with a focus on reliability efficiency and testability
Technical Stack
Transformation:DBT (core tooling for this role)
Orchestration:Dagster
Warehouse:Redshift
Language:Python (primary) SQL (advanced)
Infrastructure:Kubernetes Terraform
What Is Required
4 or more years of experience in data engineering analytics engineering BI engineering software engineering or data science
Strong Python skills with a focus on writing clean high quality maintainable code
Advanced SQL skills including writing tuning and debugging queries against large multi-source datasets
Proven experience building ETL or ELT pipelines that are reliable monitored and easy to maintain
Experience gathering requirements from stakeholders and translating them into well structured datasets
Comfortable communicating and collaborating across both technical and non-technical teams
Familiarity with the majority of the tech stack listed above
What Strengthens an Application
Experience contributing to or building out an analytics engineering practice not just inheriting one. Candidates who have thought about data modeling standards documentation culture and self-service enablement alongside pipeline engineering will stand out. Ownership mindset matters as much as technical depth here.
Working Model and Location
This role is based in Barcelona Spain working hybrid with two fixed office days per week (Mondays and Thursdays) at the office. Relocation assistance is available for candidates moving to Barcelona. Full remote is not available for this position. Please submit your CV in English.
The RoleThis is a foundational analytics engineering position within a data platform team that sits at the center of the business. The work involves building and maintaining the data infrastructure pipelines and self-service tooling that analysts data scientists and business stakeholders rely on dai...
The Role
This is a foundational analytics engineering position within a data platform team that sits at the center of the business. The work involves building and maintaining the data infrastructure pipelines and self-service tooling that analysts data scientists and business stakeholders rely on daily. The team is building out its analytics engineering function from the ground up which means real ownership and direct influence over how data flows across the organization.
This role is based in Barcelona Spain. Relocation assistance is available. The working model is hybrid with two days per week in the office.
What the Work Looks Like Day to Day
Design build and maintain reliable tested and well documented ETL and ELT pipelines and DBT models
Work with stakeholders across the business to understand data needs and translate them into high quality reusable data products
Identify gaps and improvement areas across the data ecosystem and shape the analytics engineering roadmap
Build and maintain self-service tools and datasets that allow engineers data scientists and analysts to get to insights quickly
Partner with Product Engineering Data Science and Analytics teams to enable effective use of the data platform
Investigate and resolve data related bugs and technical issues in platform tooling and DBT models
Contribute to and lead cross-team data projects with a focus on reliability efficiency and testability
Technical Stack
Transformation:DBT (core tooling for this role)
Orchestration:Dagster
Warehouse:Redshift
Language:Python (primary) SQL (advanced)
Infrastructure:Kubernetes Terraform
What Is Required
4 or more years of experience in data engineering analytics engineering BI engineering software engineering or data science
Strong Python skills with a focus on writing clean high quality maintainable code
Advanced SQL skills including writing tuning and debugging queries against large multi-source datasets
Proven experience building ETL or ELT pipelines that are reliable monitored and easy to maintain
Experience gathering requirements from stakeholders and translating them into well structured datasets
Comfortable communicating and collaborating across both technical and non-technical teams
Familiarity with the majority of the tech stack listed above
What Strengthens an Application
Experience contributing to or building out an analytics engineering practice not just inheriting one. Candidates who have thought about data modeling standards documentation culture and self-service enablement alongside pipeline engineering will stand out. Ownership mindset matters as much as technical depth here.
Working Model and Location
This role is based in Barcelona Spain working hybrid with two fixed office days per week (Mondays and Thursdays) at the office. Relocation assistance is available for candidates moving to Barcelona. Full remote is not available for this position. Please submit your CV in English.