Data Engineer: Build Pipelines & Analytics (Hybrid)

Yoto Limited; United Kingdom; Hybrid; GBP 60,000 - GBP 70,000; Full time

Job summary

A leading innovative children’s audio company in the UK is seeking a Mid-Level Data Engineer to build robust data pipelines and optimize data models. This role involves using tools like Dagster and dbt while collaborating with diverse teams to ensure data quality. The company promotes a flexible hybrid work model with a commitment to diversity and inclusion, alongside competitive benefits. Ideal candidates will have strong SQL and Python skills, along with experience in data engineering practices.

Benefits

Hybrid working

Life Assurance

Private Medical Insurance

25 days holiday plus bank holidays

Volunteer days

Employee referral bonus

Qualifications

• Proven experience in a data engineering and/or analytics engineering role.
• Experience with orchestration tools like Dagster or Airflow.
• Excellent SQL and strong Python expertise.
• Experience with core git workflows and version control.
• Experience working cross-functionally with data users.
• Comfortable with ambiguity in decision-making.

Responsibilities

• Build robust and scalable data extraction pipelines.
• Create and optimize data models for reporting and analytics.
• Work with Dagster for pipeline management.
• Partner with data scientists for dataset integrity.
• Establish data governance principles.

Skills

SQL

Python

Data Engineering best practices

Git workflows

Cross-functional collaboration

Tools

Dagster

dbt

APIs

Job description

A leading innovative children’s audio company in the UK is seeking a Mid-Level Data Engineer to build robust data pipelines and optimize data models. This role involves using tools like Dagster and dbt while collaborating with diverse teams to ensure data quality. The company promotes a flexible hybrid work model with a commitment to diversity and inclusion, alongside competitive benefits. Ideal candidates will have strong SQL and Python skills, along with experience in data engineering practices.