Cloud Data Engineer – Hybrid/Remote (Azure, Databricks)

Pearson Carter; Reading; Hybrid; GBP 55,000 - 75,000; Full time

Benefits offered by this job

Hybrid work

Flexible hours

Certification support

Modern Microsoft cloud tech

Job summary

Pearson Carter is seeking a Data Engineer to join a hybrid/remote role within a growing data function. You will build scalable cloud-based data pipelines, collaborate with analysts and engineers, and help deliver data platforms that support BI, reporting, analytics and future AI capabilities.

The ideal candidate has up to 2 years’ experience in data/analytics, strong SQL, Python, and ETL/ELT knowledge, plus a passion for cloud technologies and data engineering best practices.

Qualifications

• 2 years’ experience in Data Engineering or related tech role.
• Strong SQL and relational database knowledge.
• Experience with Python or similar data-focused language.
• Understanding of ETL/ELT concepts and data integration.

Responsibilities

• Develop and maintain data pipelines ingesting data from multiple sources.
• Build scalable ETL/ELT processes and data integration solutions.
• Assist with design and maintenance of enterprise data platforms.
• Develop data models and datasets to support reporting and analytics.
• Monitor pipeline performance and data quality issues.
• Collaborate with Data Analysts, Software Engineers and stakeholders.

Skills

SQL

Python

ETL/ELT

Data modelling

Data quality

Git

Cloud technologies

Agile delivery

Stakeholder engagement

Tools

Git

Azure Data Factory

Databricks

Power BI

APIs

Docker

Kubernetes

Job description

Pearson Carter is seeking a Data Engineer to join a hybrid/remote role within a growing data function. You will build scalable cloud-based data pipelines, collaborate with analysts and engineers, and help deliver data platforms that support BI, reporting, analytics and future AI capabilities.

The ideal candidate has up to 2 years’ experience in data/analytics, strong SQL, Python, and ETL/ELT knowledge, plus a passion for cloud technologies and data engineering best practices.