Lead risk data infrastructure to LLM-agent-ready state. Own the full stack of data quality across 40+ analytical tables in Snowflake, spanning 4 product lines and multiple provisioning frameworks (MxGAAP, IFRS, internal). Define and enforce SLAs, build observability for datamarts across all DMBOK quality dimensions (freshness, completeness, accuracy, consistency, timeliness, validity, uniqueness), and drive metadata and lineage completeness. Choose and maintain the right tooling (Git-based, wiki, or hybrid) for documenting schemas, business concepts, query patterns, and pitfalls
Design and evolve the LLM context layer. We already maintain a structured knowledge base covering risk data tables, business concept definitions, and query patterns — your job is to scale and formalize this into a production-grade context layer that AI agents can autonomously consume. You will shape how both humans and machines reliably query, interpret, and act on the same data infrastructure
Be an agent of change across departments. Lead by example in interactions with DWH, Finance, and other teams. Create data quality requirements, push for standards adoption, and translate risk analytics needs into concrete data platform requirements. You represent risk’s data interests externally
Lead a team of 2–3 analytics engineers. Manage their work, grow their skills, and ensure delivery on datamart development, ETL pipelines, and data quality initiatives. Build a culture of ownership and technical excellence within the team
Build a group-wide risk data governance framework. As Plata expands into new countries, design and implement data governance standards that scale across geographies. Define reusable patterns for risk data infrastructure that can be rolled out to new markets. The scope of both the framework and the team will grow with the company