Senior Data Engineer

Project description

Join our Development Center in Bucharest, and become a member of our open-minded, progressive and professional team. In this role you will be working on projects for one our world famous clients.

You will have a chance to grow your technical and soft skills, and build a thorough expertise of the industry of our client.

On top of attractive salary and benefits package, Luxoft will invest into your professional training, and allow you to grow your professional career.

Responsibilities

• 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.

  Key Responsabilities:

• 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 optimize 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 to build and operate modern data platforms.
• Optimize performance and reliability: Troubleshoot complex pipeline issues and continuously improve compute, storage, and processing performance.
• Collaborate using strong engineering practices: Work with engineering, analytics, and business teams while contributing to CI/CD, code reviews, and testing standards.

SKILLS

Must have

• 5+ years of experience as a Data Engineer, building scalable data pipelines and working with cloud-based data 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 modeling, ELT/ETL patterns, and data quality best practices.
• Experience with Google Cloud Platform, particularly 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.
• Ability to troubleshoot and optimize performance across compute, storage, and processing layers.

Nice to have

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