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Lead enterprise software sales across an assigned territory, including prospecting, business development, partner-channel development, customer acquisition, expansion, forecasting, negotiations, and contract closing. The role owns strategic sales plans, revenue targets, new-logo acquisition, direct and indirect sales channels, and performance reporting. Candidates need deep experience selling data integration, analytics, and management solutions to enterprise customers, with the ability to communicate technical and business value to executives and technical stakeholders.

Develop full-stack applications and deploy AI/ML models using Python, Go, React.js, Angular, cloud platforms, SQL, Docker, and microservices. Build complex database queries, implement CI/CD pipelines, apply DevOps practices, and secure APIs with OAuth2 or JWT. Collaborate effectively, communicate clearly, solve complex problems, and take ownership of quality and delivery.

Architects and builds enterprise Snowflake data platforms end to end, including ingestion, ELT, data modeling, semantic layers, BI enablement, governance, security, optimization, and observability. Leads modernization and migration efforts, integrates multi-source data, improves performance and cost efficiency, applies AI-enabled engineering workflows, and mentors teams while partnering with stakeholders on technical and business requirements.

Serves as the client-facing IT risk and governance lead for control-testing automation. Leads discovery sessions, maps controls to source data, identifies gaps and dependencies, defines thresholds, prioritizes implementation, and translates regulatory requirements into testable control logic with technical teams. Reviews solution designs, supports risk and audit requirements, and prepares implementation documentation. Requires extensive experience with IT risk, GRC, SOX ITGC, NYDFS, access controls, vulnerability management, asset management, Azure, and data mapping.

Lead the end-to-end architecture and implementation of a Snowflake-based Customer Data Platform. Define data flows, models, governance, security, privacy, integrations, and activation layers supporting customer analytics, personalization, and AI use cases. Partner with engineering, product, marketing, and analytics teams, evaluate data tools, optimize Snowflake performance and costs, guide CI/CD practices, mentor data engineers, and communicate architecture decisions to technical and business stakeholders.

Leads and mentors data engineering teams while overseeing data architecture, pipelines, quality, governance, security, and compliance. Collaborates with cross-functional stakeholders to define requirements, ensure data integrity and availability, and align initiatives with business goals. Drives ETL, data modeling, warehousing, orchestration, and adoption of emerging technologies.

Designs enterprise Snowflake architectures and data models for legacy-platform modernization. Leads medallion architecture, dbt standards, AWS ingestion, orchestration, security, performance optimization, migration planning, validation, reconciliation, parallel runs, and cutover decisions. Partners with client architecture teams, guides implementation teams hands-on, and applies AI-assisted conversion approaches.

Own product value delivery by gathering requirements, creating user stories, prioritizing the backlog, coordinating Agile development, validating completed work, analyzing performance and feedback, and maintaining alignment between stakeholders, customers, engineering, and business strategy.

Leads architecture standards, data modeling, and solution strategy across enterprise data modernization programs. Designs Snowflake and Databricks platforms, legacy migrations, validation and reconciliation frameworks, parallel runs, and cutovers. Chairs design reviews, advises client architecture leaders, and directs an enablement team creating reusable accelerators and playbooks. Requires deep MarTech expertise in identity resolution, customer data, audience segmentation, suppression, and campaign flows, while promoting AI-enabled engineering practices.

Leads the design, development, deployment, and monitoring of production-grade AI systems. Builds RAG pipelines, generative AI and conversational agents, multi-agent orchestration, automation workflows, forecasting models, and anomaly detection solutions. Owns the full AI lifecycle from data preparation through deployment and evaluation while optimizing performance, fairness, robustness, and explainability. Collaborates with technical and product teams to deliver AI-powered business solutions.