Top Tech Jobs & Startup Jobs in NYC, NY

The Data Product Analyst will own Signals data products, work with AI and Data Engineering, ensure data quality, conduct analyses, and shape product improvements through client feedback.

Manage and grow enterprise Technology/Cloud accounts by owning renewals, driving upsells and cross-sells, building executive relationships, coordinating with Solutions/Product teams, maintaining pipeline/forecast in CRM, and meeting revenue and retention targets.

Lead end-to-end development of AI-enabled, full‑stack products and production AI Agent systems. Own architecture, APIs, observability, and deployments; collaborate with product and stakeholders; balance rapid prototyping with scalable engineering; integrate LLMs and agent loops; mentor others and ensure reliability, performance, and customer-focused UX.

The Sales Development Representative will drive client engagement through outbound communication, qualify leads, and collaborate with Marketing to enhance conversion processes.

The Director of Strategic Data Partnerships will identify and execute exclusive data partnerships, engaging with executives to build alignment and negotiate complex agreements. This role focuses on strategic judgment and ongoing relationship management to drive product development and growth.

As a Sales Executive, manage the entire sales process, qualify leads, and close opportunities, focusing on enterprise subscription sales with a strong emphasis on client relationships.

Outbound-focused SDR responsible for prospecting Finance, Operations, and IT leaders via cold calls, emails, and LinkedIn to generate meetings. Use sales tools (CRM, calling/email platforms), refine messaging, and work with sales leadership to progress toward full-cycle sales.

Lead AI product strategy for investor workflows, building AI-native products (agents, copilots, MCPs, APIs) that turn proprietary data into reliable, production-grade investor tools. Partner with customers, engineering, design, and research to run experiments, define quality/trust frameworks, and ship 0→1 products that automate and augment investment decision-making.

Owner of high-impact data products for corporate clients: design and run QA processes, investigate and resolve complex data quality issues, collaborate with Insights and Engineering, communicate with stakeholders, and identify opportunities to automate and improve workflows while mentoring peers.

Lead a hands-on data engineering team to build and scale production data pipelines, datasets, and observability for alternative-data products. Drive architecture, data modeling, QA, and AI-ready dataset design while coaching engineers, partnering with product and research, and owning operational excellence across Databricks, Airflow, SQL, and PySpark.