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Personal Banker Safe Act in New York

32 active opportunities · Updated October 2026

Explore current personal banker safe act jobs in New York. Filter by work mode, employment type, experience, department, date posted and distance.

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📍 New York, New York, United States· Full-time
✓ High-confidence listingCompany trend -84.7%

From $192K/yr

Quick readStrong listing-quality and freshness signals

Today, everyone from PMs to Sales to SREs uses AI to query their business data - but every answer is a one-off with no governance, no consistency, and no visibility for data teams. We're building the system that fixes this: a context layer that learns from existing data tools driving high quality and consistent answers, an AI-powered query experience, full governance for data teams, and rich analysis surfaces to present and share the results. You'd be building a zero to one product of what we believe will become a major new product line for Datadog. At Datadog, we place value in our office culture - the relationships and collaboration it builds, and the creativity it brings to the table. We operate as a hybrid workplace to ensure our Datadogs can create a work-life harmony that best fits them. What you'll do Drive the product vision and roadmap spanning the Data agent experience, the semantic/context layer, governance console, chat experience and analysis surfaces (dashboards, notebooks, sheets) Own the end-to-end product lifecycle from discovery to launch, working with a cross-functional team of engineers, designers and other PMs Deeply understand the needs of two key personas: data teams (who govern and curate) and data consumers (PMs, engineers, SREs, executives who ask questions) Design the context and governance layer that makes AI-powered analytics trustworthy - including auto-generation from existing BI tools, eval frameworks, confidence scoring, and self-improvement loops Define how observability data and business data come together to serve unique use cases Engage directly with early customers and internal dogfooding users to iterate on accuracy, usability, and trust Independently research the competitive landscape across legacy BI vendors, warehouse-native analytics, AI-first startups, and AI labs Work on the product pricing Work with GTM teams to define positioning, packaging, and the path to displacing entrenched tools Who you are 5+ years of experience

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D
📍 New York, New York, United States· Full-time
✓ High-confidence listingCompany trend -84.7%

From $192K/yr

Quick readStrong listing-quality and freshness signals

Today, organizations struggle to ensure their most important digital experiences are working as intended because observability data is scattered across products and siloed in logical layers of their technical stack. We're building the system that fixes this: Journey Monitoring, a single centralized hub that brings together Real User Monitoring (RUM), Synthetics, and Product Analytics to bridge the gap between system health and user success. You'd be building a product that establishes a new functional observability layer, enabling teams to auto-discover journeys, track conversions and uptime side-by-side, and trace root causes across their full stack without cross-team handoffs. What you'll do: Drive the product vision and roadmap spanning the Journey Monitoring hub, including the journey map, details reports, automatically inferred journeys, and cross-product data federation. Own the end-to-end product lifecycle from discovery to launch, working with a cross-functional team of engineers, designers, and other PMs across the Digital Experience Monitoring (DEM) products. Deeply understand the needs of distinct key personas: SREs and engineers (who monitor uptime and troubleshoot technical performance), product managers (who track conversion and investigate behavioral drop-offs), and business leaders (who care about outcomes and revenue impact). Design the functional observability layer that makes end-to-end flow monitoring intuitive — including the auto-discovery of user journeys from real traffic, Experience Level Objectives (XLOs), and seamless Bits AI integrations for automated root-cause investigations. Define how behavioral data and technical data come together to automatically surface whether a drop in conversion is caused by a technical failure (like a breached SLO) or a behavioral friction point. Engage directly with early customers and internal dogfooding users to iterate on usability, refine the journey map, and optimize the cross-sell and up-sell paths for

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