Our client is a global technology and digital transformation organization specializing in software engineering and IT consulting, serving international clients across multiple industries. The company operates in a fast-paced, collaborative environment, with a strong focus on innovation, engineering excellence, and continuous professional development.
About the role
We are looking for a Senior Data Engineer to design and deliver scalable data pipelines and high-performance analytical solutions using SQL/BigQuery, Spark/PySpark, and Python on Google Cloud.
This role focuses on building reliable, cloud-native data products that enable advanced reporting, analytics, and decision-making across the organization
Mandatory skills
• 5+ years of experience as a Data Engineer, building scalable data pipelines and working with cloud-based data ecosystems
• Expertise in SQL and hands-on experience building performant datasets in BigQuery or similar cloud data warehouses
• Proficiency in Python and PySpark for scalable data processing in distributed environments
• Understanding of data modeling, ELT/ETL patterns, and data quality best practices
• Familiarity with Google Cloud Platform, particularly BigQuery, Dataflow, and Cloud Composer, GCS, or equivalent cloud data services
• Background in building scalable data pipelines, both batch and near real-time, in a cloud-native environment
• Proficiency with version control, CI/CD pipelines, and automated testing frameworks
• Capability to troubleshoot and optimize performance across compute, storage, and processing layers
Nice-to-have skills
• Experience with Infrastructure-as-Code, including Terraform, Ansible, and Chef
• Knowledge of shell scripting
• Experience in financial services or regulated environments
• Design and deliver batch and real-time ETL/ELT pipelines across cloud environments to support analytics and reporting
• Write and optimize advanced SQL transformations and build performant, cost-efficient BigQuery data models
• Implement scalable data processing solutions using Python and PySpark, ensuring maintainable and high-quality code
• Build robust data models and apply validation practices to maintain accuracy and reliability
• Use GCP services such as BigQuery, Dataflow, Cloud Composer, Pub/Sub, and GCS to build and operate modern data platforms
• Troubleshoot complex pipeline issues and continuously improve compute, storage, and processing performance
• Collaborate with engineering, analytics, and business teams while contributing to CI/CD, code reviews, and testing standards
