Job description

IPRoyal builds data access and data-mining infrastructure trusted by thousands of organizations worldwide - from fast-growing startups to large enterprises.

Our products enable customers to reliably collect and work with publicly available data at scale, solving complex challenges around availability, performance, and compliance. We operate globally, at high volume, and under demanding reliability requirements.

As our customers and use cases evolve, so does the complexity of our platform - which is why our data infrastructure and analytics capabilities are becoming a critical growth lever for the company.

We're looking for a Data Engineer with 2–5 years of experience to join our data team. You'll play a key role in managing and evolving our data infrastructure on Google Cloud Platform, with a strong focus on data governance, dimensional data modeling, and complex data transformations within our dbt project. This is a hands-on role where you'll work closely with analysts and stakeholders to ensure our data platform is reliable, well-governed, and scalable.

• Own and develop complex dbt models - build, optimize, and maintain data transformations across staging, intermediate, and mart layers following dimensional modeling best practices

• Enforce and improve data governance practices - implement data contracts, documentation standards, column-level lineage, access controls, and data quality testing within dbt

• Design and maintain data pipelines using workflow orchestration tools

• Write and maintain Python scripts for data ingestion, processing, and integration tasks

• Manage and optimize the data warehouse for cost, performance, and structure

• Collaborate on Infrastructure as Code to provision and manage cloud resources

• Establish and maintain data quality frameworks - implement dbt tests, source freshness checks, and anomaly detection

• Contribute to defining and enforcing naming conventions, coding standards, and PR review processes for the dbt project

• Strong proficiency with dbt - you understand ref/source patterns, incremental models, macros, packages, and testing strategies

• Solid experience with SQL and at least one MPP/columnar database (BigQuery, Snowflake, Redshift, or similar)

• Hands-on experience with dimensional data modeling - star schemas, snowflake schemas, slowly changing dimensions, fact/dimension table design

• Working knowledge of Python for data tasks (scripting, APIs, lightweight ETL)

• Experience with a workflow orchestration tool (Dagster, Airflow, Prefect, or similar)

• Familiarity with cloud platform services (GCP, AWS, or Azure - compute, storage, IAM)

• Understanding of data governance concepts - ownership, access control, documentation, data quality

• Experience with Git-based workflows and code review practices

• Experience with Terraform or other IaC tools for managing cloud infrastructure

• Experience implementing CI/CD pipelines for dbt (e.g., dbt Cloud, GitHub Actions)

• Exposure to data cataloging or metadata management tools

• Understanding of data privacy regulations (GDPR or similar)
  
  

• Cloud Platform: Google Cloud Platform (GCP)

• Data Warehouse: BigQuery

• Transformation: dbt

• Orchestration: Dagster

• Infrastructure as Code: Terraform

• Scripting & Integration: Python

• Data Ingestion: Airbyte

• Real ownership: Work on a data platform that directly impacts product and business decisions.

• Visible impact: Your work will enable teams across the company to make better, faster decisions.

• Builder culture: Fast decisions, low bureaucracy, and a high-trust environment.

• Learning & growth: Opportunity to deepen expertise in modern data stack and large-scale data systems.

• Resources to Win: Budgets for tools, learning, and professional growth — plus support for work equipment and other essentials.

• Exclusive Perks: Enjoy a range of benefits, from snacks and performance bonuses to team workations and special company experiences.

• Salary range: €4,000 – €6,000 gross / month