Junior Data Engineer - Mobile Apps
SpainFull-TimeJunior
Salary not disclosed
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Job Details
- Experience
- 1+ year
- Required Skills
- DockerPythonApache AirflowJenkinsTerraformBigQuerydbtGitHub Actions
Requirements
- 1+ year of experience in data engineering or a related data role.
- Basic hands-on experience with GCP services such as BigQuery and Google Cloud Storage.
- Familiarity with Apache Airflow for scheduling and orchestrating data workflows.
- Some experience with dbt or similar transformation tools.
- Understanding of Data Mart concepts and interest in Infrastructure as Code tools such as Terraform.
- Good coding skills in Python; Java or Scala is a plus.
- Familiarity with Docker and basic container concepts.
- Exposure to CI/CD and version control workflows such as GitHub Actions, GitLab CI, Jenkins, or similar.
- Understanding of data quality principles and experience with dbt tests, Great Expectations, or similar tools is a plus.
- Basic knowledge of data governance concepts such as lineage, metadata, and access control.
- Clear communication skills and willingness to work closely with technical and non-technical stakeholders.
- Strong problem-solving mindset and attention to detail.
Responsibilities
- Support the development and maintenance of our data platform on GCP, including data warehousing in BigQuery/Databricks and data lake storage in Google Cloud Storage.
- Help organize data into clear layers and domain-focused Data Marts for analytics and reporting.
- Assist with Terraform-based Infrastructure as Code to provision and manage cloud resources in a consistent way.
- Contribute to batch and near real-time data workflows with a focus on reliability, scalability, and cost awareness.
- Build, maintain, and improve ETL/ELT pipelines under guidance using Apache Airflow for workflow orchestration.
- Develop and maintain dbt transformations to create clean, version-controlled data models in BigQuery.
- Support data ingestion and processing using tools such as Google Dataflow, Apache Beam, or Pub/Sub where needed.
- Monitor scheduled jobs, troubleshoot failures, and help ensure data is delivered on time for analytics and reporting.
- Help implement and maintain data quality checks using Great Expectations, dbt tests, or similar tools.
- Partner with Analytics, Product, and Data Science teams to provide reliable datasets for dashboards, reporting, and experimentation.
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