Data Engineer

Required skills and experience

• Strong hands-on experience with GCP services, especially BigQuery, Cloud Storage, and Composer for data pipeline orchestration. 
• Expertise in Databricks with PySpark for building and optimizing scalable ETL/ELT pipelines. 
• Proficient in writing and optimizing complex SQL queries for data transformation, aggregation, and reporting on large datasets. 
• Experience integrating and processing data from multiple sources (APIs, cloud storage, databases) into a central data warehouse. 
• Good understanding of workflow orchestration tools like Apache Airflow or Cloud Composer for scheduling and monitoring data jobs. 
• Familiar with version control (Git), CI/CD practices, and Agile methodologies in a data engineering environment. 
• Technical Skills 
• Advanced proficiency in Databricks and PySpark for large-scale data processing. 
• Strong command over SQL, with experience in BigQuery or equivalent distributed databases. 
• Comfort working with structured and semi-structured datasets (JSON, Parquet, etc.). 
• Hands-on experience in designing, deploying, and maintaining data pipelines in cloud-native environments. 
• Analytical and Domain Skills 
• Strong data analysis skills to investigate anomalies, validate scoring logic, and derive insights from ESG fund data. 
• Exposure to ESG concepts or financial products like mutual funds, ETFs, and indices (preferred but not mandatory). 
• Ability to translate business rules into technical logic and validation mechanisms. 
• Soft Skills and Collaboration 
• Problem-solving mindset with strong debugging and root cause analysis skills. 
• Excellent communication and documentation abilities to work across global, cross-functional teams. 
• Self-driven, proactive attitude with ownership of critical workflows and timelines.