• Define required tags, allowed values, and tagging standards (dataset/table/column), including inheritance rules. • Map database objects to logical names and plain-language definitions aligned to mission and functional use. • Translate governance requirements into implementable tagging rules and measurable acceptance criteria. • Define policy-enabling tags that support ABAC/ICAM enforcement use cases (e.g., classification, CUI/PII, dissemination/release constraints). • Specify minimum provenance/lineage fields needed to explain source, movement, and transformations for auditability. • Set metadata quality gates for completeness and correctness; manage and document exceptions/deviations with a clear workflow. • Provide rules, templates, and patterns that enable automation (pattern-based tagging, rulesets, validation checks). • Validate export structures for catalog ingestion readiness (required fields, traceability) and support JSON/XML/CSV outputs. • Brief stakeholders, support PoC demonstrations, and capture feedback for scaling beyond the PoC dataset.

Senior AI Data Engineer (Agentic Systems) Location: USA / Europe / Israel - with a 5hour overlap with EST hours Compensation: $130K - $150K We build the technology that powers safer, more accessible financial markets. Our risk management systems, oracles, and AI models currently secure over $200 billion in assets across the world's largest decentralized protocols, having processed more than $5 trillion in transaction volume. We recently launched a pioneering Financial Intelligence Platform that transforms complex market data into actionable insights, bringing institutional-grade intelligence to every participant in the ecosystem. The Role: We are looking for a Senior AI Data Engineer to design and build the agentic systems powering our intelligence platform. You will work at the intersection of LLMs, financial data, and production infrastructure, creating intelligent agents that reason, plan, and execute across complex financial workflows. Responsibilities: - Agentic Systems: Design and build single and multi-agent systems incorporating planning, memory, and tool use. - Infrastructure: Build and operate MCP servers with secure schemas and permissions. - Workflows: Develop sophisticated agentic workflows using LangGraph or equivalent frameworks. - LLM Integration: Manage prompts, structured outputs, and tool calling via SDKs. - Evaluation: Define and run LLM evaluation pipelines for quality, correctness, latency, cost, and regressions. - Observability: Build reliability infrastructure, including logging, tracing, retries, and state management. - Performance: Optimize performance and cost-efficiency from prototype to production. - Mentorship: Establish agentic best practices and mentor junior engineers.

• Design, develop, and optimize EDP data pipelines using Python, Airflow, DBT, and Snowflake for scalable financial data processing. • Build performant Snowflake data models and DBT transformations following best practices, standards, and documentation guidelines. • Own ingestion, orchestration, monitoring, and SLA-driven workflows; proactively troubleshoot failures and improve reliability. • Implement robust data quality, validation, reconciliation, and governance controls across datasets. • Lead small-to-medium pipeline enhancements, refactoring initiatives, and cost/performance optimization efforts. • Collaborate with analysts and platform teams, mentor junior engineers, participate in reviews, and contribute to reusable EDP frameworks.

• Develop and orchestrate data pipelines using Azure Data Factory/Synapse. • Build data warehousing and perform analytics engineering using Azure Synapse Analytics. • Optimize T-SQL queries and implement indexing strategies. • Set up ETL/ELT frameworks with error handling and incremental loads. • Integrate various data formats from different sources. • Utilize source control and deployment practices for team collaboration.