Senior Data Engineer: Build Scalable Cloud Data Pipelines

Jacobs; Manchester; Hybrid; GBP 65,000 - 90,000; Full time

Benefits offered by this job

Pension

Holiday entitlement

Flexible working

Development opportunities

Mentoring

Hybrid working policy

Inclusive culture

Job summary

Jacobs is seeking a Data & Digital Engineer to design, build and maintain cloud-based data platforms that unlock business value. You will work with AWS and Azure services, develop ETL/ELT pipelines, and collaborate with engineers, analysts and client stakeholders to deliver impactful data solutions.

You will apply PySpark, SQL and modern engineering practices to ensure data quality, performance and scalable delivery across complex environments, while embracing flexible and hybrid working

Qualifications

• Experience designing, building and maintaining ETL/ELT data pipelines.
• Strong SQL skills for data validation, querying and performance tuning.
• Proficiency in Python or Java for data processing tasks.
• Familiarity with CI/CD practices and DevOps in data environments.

Responsibilities

• Design, build and maintain reliable ETL/ELT pipelines on AWS/Azure.
• Develop data integration components and microservices where needed.
• Transform and validate structured and semi-structured data (Parquet, Avro, JSON).
• Write efficient queries across Athena, Redshift, Synapse SQL.
• Create scalable data processing with PySpark and cloud orchestrations.
• Implement data quality checks, monitoring and logging for trusted data delivery.
• Collaborate with architects, analysts and stakeholders to deliver high-quality solutions.

Skills

PySpark

SQL

Python/Java

CI/CD

Tools

AWS

Azure

Data Factory/Synapse

Job description

Jacobs is seeking a Data & Digital Engineer to design, build and maintain cloud-based data platforms that unlock business value. You will work with AWS and Azure services, develop ETL/ELT pipelines, and collaborate with engineers, analysts and client stakeholders to deliver impactful data solutions.

You will apply PySpark, SQL and modern engineering practices to ensure data quality, performance and scalable delivery across complex environments, while embracing flexible and hybrid working