GCP BQ/ GCP python

Primary skills:Technology->Functional Testing->Mainframe testing->Proterm,Technology->OpenSystem->Python - OpenSystem GCP BigQuery Development Design, develop, and optimize data warehouses using Google BigQuery Build and maintain data models (fact/dimension, star/snowflake schemas) Develop complex SQL queries for analytics and reporting Optimize queries for cost, performance, and scalability Implement partitioning, clustering, and materialized views Python Development Develop data pipelines and workflows using Python Build reusable scripts for data ingestion, transformation, and automation Integrate Python applications with GCP services and APIs Ensure code quality, modularity, and scalability Data Engineering & ETL Design and build ETL/ELT pipelines using GCP tools Ingest data from multiple sources (APIs, databases, streaming platforms) Process structured and unstructured data efficiently Ensure data quality, validation, and governance GCP Cloud Services Work with GCP services such as: BigQuery (mandatory) Cloud Storage (GCS) Dataflow / Dataproc Pub/Sub (streaming) Cloud Composer (Airflow) Design scalable and secure cloud-native architectures Performance Optimization Monitor and optimize BigQuery cost and performance Troubleshoot data pipeline failures and bottlenecks Implement best practices for efficient data processing Collaboration & Leadership Collaborate with data scientists, analysts, and business stakeholders Provide technical guidance and mentorship to junior engineers Participate in architecture and design discussions Work in Agile/Scrum environments Required Skills & Qualifications Core Skills 5–9 years of experience in data engineering / big data development Strong hands-on experience with GCP BigQuery (mandatory) Strong proficiency in Python (mandatory) Expertise in SQL and query optimization Experience in building ETL/ELT pipelines Technical Skills Deep knowledge of BigQuery architecture and data modeling Experience with Airflow / Cloud Composer for orchestration Strong understanding of data warehousing concepts Familiarity with Git and CI/CD processes Knowledge of REST APIs and integrations Preferred Skills Experience with streaming (Pub/Sub, Kafka equivalent) Exposure to Dataproc / Spark / PySpark Familiarity with data lakes and lakehouse architectures Knowledge of Docker/Kubernetes Experience with BI tools (Looker, Tableau, Power BI)