Key Responsibilities

• Diagnose and resolve data issues, production outages, and performance bottlenecks to ensure system reliability.
• Debug data job failures, including pipeline breakdowns and unexpected changes in row-level data.
• Optimize database queries and monitor database health to enhance efficiency and scalability.
• Develop automated workflows for data regeneration to maintain accuracy and consistency.
• Enable secure and efficient data access for analysis, balancing openness with compliance requirements.
• Stay updated on industry trends and recommend best-in-class data engineering technologies and practices.
• Collaborate with the Data Lead and engineering team to implement optimal data ingestion and storage strategies.
• Design and build scalable, robust, and maintainable data pipelines using cutting-edge technologies in consultation with the Data Lead.

Requirements

• Minimum of 4 years of relevant working experience preferred.
• Strong proficiency in Python, with extensive hands-on experience using Pandas for data manipulation, transformation, and analysis of large datasets.
• Expertise in SQL performance tuning, database optimization, and query efficiency.
• Hands-on experience developing, deploying, and debugging robust data pipelines and ETL/ELT workflows in production environments.
• Experience with at least one cloud platform (AWS, GCP, or Azure) and data warehousing solutions.
• Strong problem-solving skills, particularly in navigating ambiguous or unknown data issues.
• Excellent communication skills with the ability to collaborate effectively with engineering and product teams.