Engineer – PySpark/SQL

This opportunity is well suited for data engineers who enjoy designing scalable data pipelines, working with cloud technologies, and solving complex data processing challenges. You'll gain exposure to enterprise-scale financial systems while building solutions that support millions of customers worldwide.

🚀 Why This Opportunity Stands Out

Working at Barclays provides exposure to one of the world's largest banking technology environments. Engineers are responsible for processing massive datasets while ensuring reliability, governance, and security. Rather than maintaining small internal applications, you'll contribute to systems that power enterprise analytics and regulatory reporting across multiple business functions.

Hybrid Permanent

🛠 Technologies You'll Use

You'll work with modern cloud-native data engineering tools, including:

Python
PySpark
SQL
Databricks
AWS Glue
Amazon Athena
Amazon S3
IAM
Git
Unix
Shell Scripting
Jira

The team follows modern engineering practices including CI/CD, Agile delivery, automated testing, monitoring, and infrastructure optimization.

📊 Your Key Responsibilities

As a Data Engineer, you'll contribute throughout the complete data lifecycle.

Responsibilities include:

• Building scalable batch and real-time data pipelines using PySpark and Python.

• Developing optimized SQL queries for large enterprise datasets.

• Designing scalable Lakehouse architectures using Databricks.

• Managing structured and semi-structured datasets efficiently.

• Optimizing Spark jobs for performance and cost.

• Implementing monitoring, validation, and alerting for production pipelines.

• Collaborating with business teams to understand reporting requirements.

• Supporting secure access management and regulatory compliance.

• Improving pipeline reliability through automation and DevOps practices.

• Participating in Agile sprint planning, reviews, and backlog refinement.

☁️ Cloud & Data Platform Exposure

This role provides hands-on experience with enterprise cloud infrastructure. Engineers are expected to design solutions that can process large volumes of financial information while maintaining high availability.

You'll gain experience with:

• Cloud-native ETL development

• Enterprise Data Lake architecture

• Lakehouse implementation

• Distributed data processing

• Metadata management

• Data governance

• Performance tuning

• Secure cloud storage

• Workflow orchestration

📈 Skills That Can Help You Succeed

Successful candidates typically possess strong analytical thinking combined with practical engineering experience.

Preferred technical skills include:

• Strong Python programming

• Advanced SQL development

• PySpark optimization

• Databricks notebooks

• AWS cloud services

• Data modeling

• Dimensional modeling

• Linux fundamentals

• Shell scripting

• Git version control

• CI/CD concepts

• Agile methodologies

Knowledge of data quality frameworks and enterprise monitoring solutions is considered an advantage.

🌍 Working Environment

Engineers collaborate with global technology teams across different regions. Daily work includes technical discussions, sprint planning, code reviews, production support, and solution design. Communication skills are important because you'll regularly interact with architects, analysts, and business stakeholders while delivering enterprise-scale solutions.

Strong engineering fundamentals combined with clean SQL and scalable Spark development are highly valued for this role.

🎯 What Recruiters May Evaluate

During interviews, recruiters may assess your understanding of:

• SQL joins and window functions

• Query optimization

• PySpark transformations

• Spark architecture

• Data partitioning

• AWS services

• ETL pipeline design

• Data warehouse concepts

• Lakehouse architecture

• Python programming

• Data structures

• Performance tuning

• Problem-solving ability

• Communication skills

📚 How This Role Can Grow Your Career

Working in Barclays' technology division can significantly strengthen your experience in enterprise data engineering. Exposure to distributed processing, cloud-native architecture, financial datasets, and production-scale systems provides valuable experience for future roles such as Senior Data Engineer, Big Data Engineer, Cloud Data Engineer, Analytics Engineer, or Data Platform Engineer.

🔑 Keywords for Resume

Python • PySpark • SQL • Databricks • Apache Spark • AWS Glue • Amazon Athena • Amazon S3 • IAM • ETL • Data Engineering • Data Lake • Lakehouse • Data Warehouse • Shell Scripting • Linux • CI/CD • Git • Agile • Jira • Data Modeling • Performance Tuning • Spark Optimization • Cloud Computing

💡 Final Thoughts

This opportunity is ideal for professionals looking to work with modern cloud data platforms while solving large-scale engineering challenges. Engineers joining Barclays will gain practical experience building secure, scalable, and high-performance data solutions in a global financial technology environment.