Senior GCP Data Engineer
Summary
Build and maintain scalable GCP data pipelines and analytics platforms for a digital banking project using BigQuery, Dataflow, and PostgreSQL.
Project description
We are looking for the best people to help create the next big thing in digital banking. Reason to join us • We offer the opportunity to work in a highly professional environment where you will work with high-level financial instruments • We want you to be part of our success story and give you reasons to be proud of what you achieved as part of our fabulous team • We give you the opportunity to develop yourself and evolve in your career via our fantastic technical, business-related or soft skills training • We encourage creative-thinking in our great open-minded work environment. Frequently the relaxation rooms are the place where the most ambitions ideas are born. • We are not just professional teams, we are also friends who have fun working together • The team is made up of enthusiastic professionals who work in an international environment, learning new technologies as part of client`s businesses.
Responsibilities
- Data Engineering & Architecture
- Design, develop, and maintain scalable data platforms and pipelines using Google Cloud services.
- Implement data ingestion, transformation, and orchestration workflows using: o BigQuery o Dataflow o Dataproc o Pub/Sub o Cloud Storage o Cloud SQL / PostgreSQL
- Define data models, partitioning strategies, and storage architectures optimized for large-scale analytics.
- Implement batch and near real-time data processing solutions. BigQuery Engineering
- Design, optimize, and maintain BigQuery datasets, tables, views, and data marts.
- Develop complex BigQuery SQL solutions for analytics and operational reporting.
- Optimize query performance, storage costs, partitioning, clustering, and workload management.
- Establish best practices for BigQuery governance, security, and performance monitoring. PostgreSQL Engineering
- Design and develop PostgreSQL database solutions on GCP.
- Create and optimize database schemas, indexes, stored procedures, and PL/pgSQL code.
- Perform database performance tuning, capacity planning, and troubleshooting.
- Support migration and modernization of legacy databases to PostgreSQL. Cloud Migration & Modernization
- Support migration of legacy Oracle workloads to PostgreSQL and BigQuery.
- Assess existing database architectures and recommend modernization approaches.
- Collaborate with application teams to refactor data solutions for cloud-native environments.
- Ensure data integrity, security, and operational readiness during migrations. Data Governance & Security
- Implement IAM controls, encryption, auditing, and data protection standards.
- Apply data governance and compliance requirements across the cloud platform. Collaboration
- Participate in architecture reviews, code reviews, and engineering standards definition.
- Collaborate with product owners, architects, analysts, and platform teams to deliver business value.
SKILLS
Must have
- Minimum 5 years of hands-on experience working with relational and analytical databases
- Minimum 1 year of hands-on experience with Google BigQuery (GCP)
- Proven expertise in Google BigQuery (GCP), with the ability to build, manage, and optimize data solutions in a cloud environment.
- Proven expertise in PostgreSQL administration and development, including schema design, data integrity, performance monitoring, and troubleshooting.
- Proficiency with GitHub for version control and collaborative development.
Nice to have
• Experience migrating Oracle databases to PostgreSQL and/or BigQuery. • Oracle SQL and PL/SQL knowledge. • Experience with Ora2pg or similar migration tooling. • Google Cloud Professional Data Engineer certification. • Google Cloud Professional Cloud Architect certification. • Experience with Terraform. • Experience with containerized deployments using Docker and Kubernetes (GKE). • Experience with Airflow / Cloud Composer. • Experience with data governance, metadata management, and data quality frameworks. • Experience with Google Cloud Dataflow for building and managing data processing pipelines. • Experience with Google Cloud Dataproc for big data processing and analytics workloads. • Experience with Google Cloud Pub/Sub for event-driven architectures and real-time data streaming. • Python for data engineering, automation, data processing, and scripting tasks.