Senior Data Engineer (GCP)

• At least 5 years of professional experience as a Data Engineer.
• Proven hands-on experience developing data solutions on Google Cloud Platform.
• Experience with data visualization tools such as Looker, Power BI, Tableau, or QuickSight.
• Experience with data modeling, orchestration, performance optimization, and large-scale data processing.
• Strong Python development skills, including building data pipelines and ETL/ELT processes.
• High proficiency in SQL.
• Experience designing and developing cloud-based Data Warehouses and Lakehouse solutions.
• Experience with ETL/ELT and transformation tools such as dbt, Dataform, or Rivery.
• Familiarity with CI/CD, Git, Infrastructure as Code, and production deployment practices.
• Strong analytical and problem-solving skills.
• Ability to learn new technologies independently and work across multiple projects.
• Strong experience with several of the following GCP services: BigQuery, Cloud Storage, Dataflow, Dataproc, Cloud Composer, Cloud Run, or Cloud Functions.

Nice to have:

• Hands-on experience with AWS or Microsoft Azure data services.
• Experience with AWS Glue, Redshift, EMR, Kinesis, Azure Data Factory, Synapse, or Databricks.
• Experience with real-time data processing and streaming architectures.
• Experience with Kafka or other event-driven platforms.
• Knowledge of AI and ML services such as Vertex AI, Gemini, BigQuery ML, SageMaker, Bedrock, or Azure Machine Learning.
• Relevant GCP professional certifications.
• Previous experience in consulting or customer-facing technology projects.

• Design and develop scalable data solutions on Google Cloud Platform.
• Lead the technical design and implementation of customer data projects.
• Understand business and technical requirements and translate them into effective data architectures.
• Build and maintain ETL/ELT pipelines, Data Lakes, Lakehouses, and cloud-based Data Warehouses.
• Design data models and integration processes for Batch and real-time workloads.
• Work with structured, semi-structured, and unstructured data.
• Select appropriate technologies based on performance, scalability, security, and cost.
• Implement data quality, monitoring, governance, and orchestration processes.
• Collaborate with Data Architects, Data Engineers, DevOps teams, analysts, and customer stakeholders.
• Participate in the development of analytics, AI, and ML solutions where relevant.