Data Engineer (Python & PySpark)
Key Responsibilities
Pipeline Development: Design, develop, and maintain end-to-end ETL/ELT pipelines using Python and PySpark. Big Data Processing: Build large-scale data processing frameworks to handle structured and unstructured data, ensuring high performance and reliability. Cloud Infrastructure: Architect and manage data solutions within the GCP ecosystem, focusing on cost-efficiency and security. Data Modeling: Design and implement robust data warehouse models (Star/Snowflake schemas) and data lake architectures. Optimization: Identify, design, and implement internal process improvements, such as automating manual processes and optimizing data delivery for greater scalability. Collaboration: Work closely with stakeholders to understand data requirements and translate them into technical specifications. Technical Qualifications Core Programming: Strong proficiency in Python, including experience with libraries like Pandas, NumPy, and logging frameworks. Big Data: 3+ years of hands-on experience with Apache Spark (PySpark) for distributed data processing. GCP Ecosystem: Practical experience with Google Cloud services, specifically: BigQuery (Optimization, Partitioning, Clustering). Cloud DataProc or Dataflow. Cloud Storage (GCS) and Cloud Functions. Cloud Composer (Apache Airflow) for orchestration. Data Warehousing: Solid understanding of relational databases and SQL (PostgreSQL, MySQL) as well as NoSQL environments. DevOps & Tools: Experience with Git, Docker, and CI/CD pipelines. Familiarity with Terraform or other IaC tools is a significant plus.