Data Engineering Tech Lead-GCP

At least 8 years of experience as a Data Engineer. Minimum 6 years of experience working with GCP cloud-based infrastructure & systems. Minimum 2 years in a Technical Leading role. Experience leading technical teams and collaborating with business stakeholders. Ability to lead discussions with clients to identify improvement avenues. Deep knowledge of Google Cloud Platform and cloud computing services. Extensive experience in designing, building, and deploying data pipelines in the cloud. Proficiency in database management systems such as SQL (BigQuery is a must), NoSQL. Ability to design, configure, and manage databases for optimal performance and reliability. Programming skills (SQL, Python, other scripting). Proficiency in data modeling techniques and database optimization. Knowledge of query optimization, indexing, and performance tuning. Knowledge of at least one orchestration and scheduling tool (Airflow is a must). Experience with data integration tools and techniques (ETL and ELT). Excellent communication skills to collaborate with cross-functional teams and convey technical concepts to non-technical stakeholders. Tools knowledge: Git, Jira, Confluence. Openness to learn new technologies and solutions. Experience in multinational environments and distributed teams.

Design, model, and develop GCP data ecosystem (Cloud Storage, Cloud Functions, BigQuery). Gather, analyze, model, and document business/technical requirements. Engage directly with clients. Guide and mentor data engineering team, providing technical direction. Oversee design and implementation of data solutions, ensuring best practices and quality standards. Train and mentor less experienced data engineers. Model data from various sources and technologies. Troubleshoot and support complex problems. Design and optimize data storage architectures. Implement techniques for data storage and retrieval optimization. Identify and resolve bottlenecks, tune queries, and implement caching strategies. Identify and resolve issues related to data processing, storage, or infrastructure. Monitor system performance, identify anomalies, and conduct root cause analysis.