Senior Data Engineer — Python & BigQuery

Job Location: Hibrid in Bucharest, 2 days WFO

Domain: Private Bank

Recruitment process:

• HR discussion
• 2 X technical discussion

About the Team

You will join the Data & Analytics stream, responsible for management reporting, regulatory & risk reporting, and advanced analytics. Our mission includes enhancing data quality via KPIs and migrating data platforms to modern, cloud-native ecosystems. We operate in an agile environment, committed to responsible data practices.

Role Overview

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 organisation.

Key Responsibilities

• Build scalable data pipelines: design and deliver batch and real-time ETL/ELT pipelines across cloud environments to support analytics and reporting. 
• Develop SQL and BigQuery solutions: write and optimise advanced SQL transformations and build performant, cost-efficient BigQuery data models. 
• Develop Python workflows: implement scalable data processing solutions using Python and PySpark, ensuring maintainable and high-quality code. 
• Design data models and ensure quality: build robust data models and apply validation practices to maintain accuracy and reliability. 
• Build cloud-native data solutions: use GCP services such as BigQuery, Dataflow, Cloud Composer, Pub/Sub, and GCS. 
• Optimise performance and reliability: troubleshoot complex pipeline issues and continuously improve compute, storage, and processing performance. 
• Collaborate using strong engineering practices: contribute to CI/CD, code reviews, and testing standards.

Requirements

• 5+ years of experience as a Data Engineer, building scalable data pipelines in cloud-based ecosystems.
• Strong expertise in SQL and hands-on experience building performant datasets in BigQuery or similar cloud data warehouses.
• Proven experience with Python and PySpark for scalable data processing in distributed environments.
• Solid understanding of data modelling, ELT/ETL patterns, and data quality best practices.
• Experience with GCP: BigQuery, Dataflow, Cloud Composer, GCS, or equivalent cloud data services.
• Hands-on experience building scalable data pipelines (batch and near real-time) in a cloud-native environment.
• Proficiency with version control, CI/CD pipelines, and automated testing frameworks.

Nice to Have

• Experience with Infrastructure-as-Code (Terraform, Ansible, Chef).
• Knowledge of shell scripting. 
• Experience in financial services or regulated environments.

What We Offer

• 24 days holiday + loyalty days + bank holidays.
• Flexible working hours and hybrid work model.
• Private healthcare and life insurance.
• A truly diverse, global working culture.
• Continuous learning and professional development opportunities.