Contribute to building and maintaining ETL/ELT data pipelines using Python and Apache Airflow, under the guidance of senior engineers.

Support the development of dbt Core models under supervision.

Help monitor pipeline runs, troubleshoot basic pipeline failures, and escalate unresolved issues following established procedures.

Support the data life cycle by assisting with data ingestion, transformation, validation, and documentation tasks.

Apply basic data management principles, including data quality checks and documentation.

Participate in Agile ceremonies and peer code reviews.

Write clean, testable code with guidance from senior team members.

Contribute to the continuous improvement of data engineering processes and documentation.

Bachelor's degree in Computer Science, Information Technology, Information Systems, or an equivalent qualification.

0–12 months of relevant technical experience; internships and academic projects are considered.

Basic understanding of Python programming.

Foundational knowledge of

SQL and relational databases

Data pipeline concepts, such as ETL/ELT

Software development lifecycle and programming fundamentals

Familiarity with version control systems, such as Git, and collaborative development practices.

Familiarity with Agile development methodologies.

Strong written and oral communication skills.

Eagerness to learn new skills and technologies, with a strong track record of conscientious, thoughtful work.

Exposure to Apache Airflow or similar orchestration tools.

Exposure to dbt Core.

Exposure to Google Cloud Platform (GCP).

Familiarity with Google Cloud Storage (GCS).

Exposure to Cloud Composer, GCP's managed Airflow service.

Exposure to Kafka or other event-driven/streaming data tools.