Google Cloud Platform Data Engineer

Key Responsibilities Design, develop, and optimize scalable data pipelines using Databricks and PySpark Build and maintain ETL/ELT workflows for both structured and unstructured datasets Collaborate with stakeholders to gather requirements and translate them into technical solutions Architect and implement data models (dimensional modeling, star schema, snowflake schema) Improve data quality, reliability, and standardization across systems Orchestrate workflows using Apache Airflow (Astro preferred) Contribute to the design and evolution of a modern lakehouse architecture Support analytics, reporting, and downstream data consumption needs Implement data governance, validation, and monitoring frameworks Drive performance tuning and optimization of data pipelines and queries Mentor junior engineers (for Lead-level candidates) Required Qualifications 8+ years (Senior) / 10+ years (Lead) of experience in Data Engineering Strong hands-on experience with Databricks and PySpark (must-have) Proficiency in Python and SQL Proven experience building and maintaining data pipelines at scale Hands-on experience with workflow orchestration tools (Airflow preferred) Strong understanding of data modeling techniques Experience working with structured and unstructured data formats (JSON, CSV, XML) Excellent problem-solving and analytical skills Strong communication skills with the ability to collaborate across technical and business teams Preferred Qualifications Experience with Google Cloud Platform , especially BigQuery Exposure to DBT (Data Build Tool) Experience with lakehouse architecture and modern data platforms Background in modernizing or rebuilding legacy data environments Experience in startup, consulting, or fast-paced product environments Familiarity with data governance and data quality frameworks