Data engineer

Summary

- Developing and maintaining Spark (PySpark) scripts, reducing engineering time by 40% and saving $50,000 . - Implementing Google Cloud Pub/Sub for distributed data storage and processing, leading to a 50% increase in data scalability and a cost savings of $75,000 in infrastructure expenses. - Utilizing GCP's Cloud SQL service for data manipulation and database management, achieving a 20% improvement in query performance and faster data insights, resulting in estimated operational cost savings of $30,000. - Implementing Kubernetes for containerization and deployment, resulting in a 40% improvement in scalability and resource utilization. - Leveraging Talend data integration and management, reducing data integration time by 30%, and achieving labor cost savings of $30,000 per year.

Expectations

Excel at designing robust data schemas that preserve data integrity and streamline analytical congruence. Adept at code management using Git and specializing in automated, time-sensitive data pipeline deployments. Expert at aligning technological solutions with business strategies, notably succeeding in annual cost-efficiencies across AWS and GCP platforms. Competent in implementing Spark algorithms that accelerate engineering cycles and reduce labor costs. Proficient in SQL query optimization and ETL/ELT automation, resulting in significant operational savings and efficiencies. Experienced in data architecture, data pipelines, data modeling, and data visualization. Skilled in a range of tools and platforms, such as BigQuery, AWS, GCP Compute Engine, Tableau, and dbt. Proficient in both AWS and GCP services, including equivalents like AWS Glue and GCP Data Fusion.

Employment Preferences

Expected Base Salary

**0,000 USD

Academic Degree

Experience

Skills

• Python
• SQL
• GCP
• AIrflow
• Pyspark
• DBT
• ETL
• Data Engineering