Edwin GTM Engineer

LogicMonitor•San Francisco, CA

3d•$158,400 - $217,800•Hybrid

About The Position

LogicMonitor® is the AI-first hybrid observability platform powering the next generation of digital infrastructure. LogicMonitor delivers complete visibility and actionable intelligence across on-premises, cloud, and edge environments. By anticipating issues before they strike, optimizing resources in real time, and enabling faster, smarter decisions, LogicMonitor helps IT and business leaders protect margins, accelerate innovation, and deliver exceptional digital experiences without compromise. Our customers love LogicMonitor's ability to bring cloud and traditional IT together into one view, as seen in minimal churn rates, expansion business, and exciting new customer references. In fact, LogicMonitor has received the highest Net Promoter Score of any IT Infrastructure Management provider. LogicMonitor also boasts high employee satisfaction. We have been certified as a Great Place To Work®, and named one of BuiltIn's Best Places to Work for the seventh year in a row! We are hiring a GTM Engineer to help build the systems that power customer lifecycle reporting and operations for a fast-scaling AI business. Customer signals span the full journey, from sales handoff through implementation, adoption, and renewal or expansion, and the team needs one trusted way to bring them together. This is a builder role for someone who is curious, business- and data-fluent, and excited about AI. The right person can take loose requirements and turn them into working prototypes or production-ready code using SQL, Python, scripts, and AI-assisted coding tools. The role blends business operations, data architecture, and technical build work. It is a strong fit for someone who can translate messy operating needs into practical workflows, reporting, and system logic that help leaders make decisions from data.

Requirements

5+ years in customer operations, lifecycle operations, CX operations, RevOps, Sales Ops, BizOps, Product Ops, data engineering, analytics engineering, systems engineering, or GTM engineering.
Strong business acumen and comfort connecting metrics to operating decisions.
Hands-on experience with SQL and at least one scripting language, preferably Python.
Experience integrating SaaS systems and REST APIs and JSON.
Salesforce data model familiarity.
Familiarity with Kantata, Jira, Pendo, Langfuse, Snowflake, Power BI, Slack, or BI tools.
Understanding of data modeling, data architecture, and how source systems feed operational reporting.
Comfort working with messy source data and reconciliation logic.
Comfort building working prototypes with AI-assisted coding tools, SQL, Python, scripts, or lightweight apps.
Basic familiarity with working on SaaS systems in AWS/Kubernetes
Strong communication skills and an eye for executive-level visual storytelling.
Ability to build quickly without overengineering.
Comfort analyzing trends, building forecasts, and turning data into operational recommendations.
Deep curiosity about AI tools, systems, and applied workflows.

Nice To Haves

Experience with operational data products or customer-facing internal systems.
Familiarity with event data, product telemetry, and usage analytics.
Experience in fast-moving AI, infrastructure, or SaaS environments.

Responsibilities

Build the canonical customer operating data layer
Connect Salesforce, Jira, Langfuse, Snowflake, product telemetry, and related sources into one usable customer view.
Surface customer health and product adoption signals so leaders can quickly see where intervention is needed.
Build executive-facing reporting, visualizations, and operating views for accounts, health, adoption, and leadership readouts.
Translate lifecycle definitions and stage entry / exit criteria into system requirements.
Build and maintain integrations, reconciliation logic, data recency checks, source-of-truth mapping, and history/backfill support.
Support automation jobs, alerts, backend reliability, and the semantic layer that powers BI and reporting surfaces.
Support AI summaries and account readouts with structured, attributed, current data.
Partner with business and technical stakeholders to turn reporting needs into workflows, data structures, and durable code.
Define requirements for stage movement, time-to-value, customer health, and product adoption.
Maintain the architecture needed for history, freshness, reconciliation, and reporting consistency.
Work closely with source-system owners and BI / data partners.
Help define field hygiene, metric definitions, and data governance for customer lifecycle reporting and operations.
Data governance mindset: you think in systems, not fixes, building and documenting standards, and hold the line on data quality over time.