Teams at Alternative Path

Monitor data support issues and pipelines within defined SLAs, troubleshoot and resolve problems using established guidelines, and escalate issues when appropriate. Document findings, provide daily and weekly status updates, improve support procedures, and collaborate with engineering, product, and client teams to maintain timely, high-quality data operations.

Own the end-to-end request process for proprietary web-scraped datasets, including gathering requirements, translating business needs into technical specifications, validating deliverables, and approving client-ready data. Use Python, SQL, MySQL, and PostgreSQL to create datasets, reports, and quality-control checks. Develop KPIs, QA processes, documentation, and workflow improvements while collaborating with investment teams, clients, and developers to resolve data-quality issues.

Design and implement Python-based automation for configuring data quality checks across vendor datasets. Use LLMs, documentation, and machine learning to identify critical fields and establish quality thresholds. Collaborate with data managers and software engineers to improve data-check coverage, resolve requirements, and support existing feeds. Develop dashboards tracking data quality coverage and compliance. The role requires enterprise Python development, Pandas-based transformations, SQL exposure, and preferably AWS, Kubernetes, S3, and Trino experience.