• Analysis, Design, implementation, and maintenance pipelines that produce business-critical data reliably and efficiently using cloud technologies. • Develop new ETLs (Extract, Transform, Load), using the current Apache Airflow. • Propose new initiatives to improve performance, scalability, reliability, and overall robustness. • Collect, process, and clean data from different sources using Python & SQL. • Work side by side with the main Architects and Developers to create and ensure best practices and guidelines are being used properly by all projects. • Assess and communicate effectively the effort for required developments. • Discover new data sources to improve new and existing data pipelines. • Be in charge of building and maintaining data pipelines and data models for new and existing projects. • Maintain detailed documentation of your work and changes to support data quality and governance. • Provide feedback and expert points of view as needed to help all data initiatives in the company. • Improve the quality of existing and new data processes (ETL), incorporating statistical process control, and creating alerts when anomalies are received from data sources at every step of the pipeline. • Create benchmark control of execution times for every pipeline, to control and identify potential availability issues.

• Focus on revolutionizing healthcare through AI and generative AI technologies. • Design, implement, and optimize ETL/ELT pipelines for diverse healthcare data sources. • Collaborate with data scientists and AI engineers to create datasets and enhance data readiness for AI/ML applications. • Manage and optimize various databases including Postges, NoSQL, graph databases, and cloud-based databases like Snowflake and Redshift. • Ensure efficient storage, retrieval, and integration of data across different systems. • Work closely with cross-functional teams to understand and address data needs.

• 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.