Data Engineer

GIOS Technology; Glasgow; On-site; GBP 60,000 - 90,000; Full time

Job summary

GIOS Technology in Glasgow, Scotland is seeking a hands-on Data Engineer to design, build, and maintain scalable data pipelines and data lakes in AWS. You will work with PySpark, Spark, Python, and CloudFormation to deliver high-performance data solutions for enterprise analytics.

The role emphasizes collaboration with Data Scientists and Analytics teams, building production-grade ETL/ELT frameworks, and implementing CI/CD with GitLab while advancing platform modernization and secure data

Qualifications

• Experience delivering production-grade data solutions.
• Strong PySpark, Spark, Python and SQL skills.
• Experience with AWS data services and IaC using CloudFormation.

Responsibilities

• Design, develop, and maintain scalable batch and real-time data pipelines using PySpark, Spark, Python, and AWS services.
• Build and optimize data lakes and data warehouse solutions ensuring data quality, security, and accessibility.
• Develop reusable, production-grade ETL/ELT frameworks and data processing solutions.
• Implement orchestration workflows using AWS Step Functions, Airflow, and other automation tools.
• Develop and maintain cloud infrastructure using AWS CloudFormation.
• Collaborate with business stakeholders to understand requirements and translate them into scalable technical solutions.
• Optimize data processing performance, monitoring, and operational support.
• Implement unit testing, code reviews, and CI/CD best practices using GitLab.
• Support platform modernization and migration initiatives leveraging Spark-based architectures.
• Work closely with Data Scientists and Analytics teams to enable AI/ML use cases.

Skills

Data Engineering

PySpark

Apache Spark

Python

SQL

ETL/ELT

CI/CD

GitLab

Stakeholder management

Troubleshooting

Problem solving

Tools

S3

Glue

Lambda

Step Functions

ECS

IAM

KMS

VPC

CloudFormation

SageMaker

Databricks

Job description

We are looking for Data Engineer at Glasgow, Scotland – 2-3 days per week Onsite

Purpose of the Role

To design, build, and maintain scalable data pipelines, data lakes, and data warehouse solutions on AWS. The role focuses on developing high-performance data engineering solutions using PySpark, Spark, Python, and AWS services, enabling secure, reliable, and efficient data processing and analytics across enterprise platforms.

Key Responsibilities

• Design, develop, and maintain scalable batch and real-time data pipelines using PySpark, Spark, Python, and AWS services.
• Build and optimize data lakes and data warehouse solutions ensuring data quality, security, and accessibility.
• Develop reusable, production-grade ETL/ELT frameworks and data processing solutions.
• Implement orchestration workflows using AWS Step Functions, Airflow, and other automation tools.
• Develop and maintain cloud infrastructure using AWS CloudFormation.
• Collaborate with business stakeholders to understand requirements and translate them into scalable technical solutions.
• Optimize data processing performance, monitoring, and operational support.
• Implement unit testing, code reviews, and CI/CD best practices using GitLab.
• Support platform modernization and migration initiatives leveraging Spark-based architectures.
• Work closely with Data Scientists and Analytics teams to enable AI/ML use cases.

Required Skills & Experience

• Strong hands-on experience in Data Engineering with delivery of production-grade solutions.
• Expertise in PySpark, Apache Spark, Python, and SQL.
• Strong experience designing and optimizing complex data pipelines and ETL/ELT frameworks.
• Hands-on experience with AWS services including:
• S3
• Glue
• Lambda
• Step Functions
• ECS
• IAM
• KMS
• VPC
• SageMaker (preferred)
• Experience with AWS CloudFormation for Infrastructure as Code.
• Strong understanding of data lakes, data warehouses, and distributed data processing.
• Experience with GitLab, CI/CD, Unit Testing, and DevOps practices.
• Excellent problem-solving skills and ability to work independently.
• Strong stakeholder management and communication skills.

Nice to Have

• Experience with Databricks, Delta Lake, Unity Catalog, and migration projects.
• Knowledge of AI/ML and MLOps frameworks.
• Experience with streaming technologies such as Kafka or Kinesis.

Ideal Candidate:

A hands-on Data Engineer with strong expertise in Spark, PySpark, AWS, and CloudFormation, capable of building scalable enterprise data solutions while driving modernization and cloud transformation initiatives.