Lead Forward Deployed Engineer

Lead Forward Deployed Engineer

Come work at a place where innovation and teamwork come together to support the most exciting missions in the world!

Key Responsibilities

Customer Onboarding & Data Integration

• Strategic Execution: Lead end-to-end enterprise onboarding from strategy to production rollout, serving as the system of record for customer assets, findings, and business context.
• Pipeline & API Design: Build REST API, webhook, and scalable ingestion (e.g., S3-based ETL/ELT) pipelines to integrate diverse data sources (VM, EDR/XDR, Cloud, IAM, CMDBs, GRC).
• Data Modeling & Correlation: Design custom mapping, transformation, tagging, and asset criticality scoring; implement de-duplication and correlation logic across heterogeneous security feeds.
• Pipeline Reliability: Ensure continuous full and incremental data ingestion with automated scheduling, monitoring, and error handling.

Data Quality & Validation

• Data Integrity: Establish scalable validation frameworks to audit and maintain asset/findings accuracy for confident risk decision-making.
• Issue Resolution: Execute controlled reprocessing and configuration adjustments to fix data quality issues without disrupting operational metrics.

Dashboards, Remediation & Workflows

• Analytics & Modeling: Build customized dashboards, trend visualizations, and risk scoring models, including complex "toxic risk combination" scenarios.
• Workflow Automation: Integrate ETM with ticketing/remediation tools (Jira, ServiceNow) and configure automated alerts, escalations, and ownership mapping.
• Executive Reporting: Develop custom reports/utilities using public APIs for board-level, operational, and regulatory audiences.

Product & Engineering Feedback Loop

• Roadmap Influence: Translate field deployment challenges and recurring integration gaps into technical requirements for product and core engineering teams.
• Customer Maturity: Guide enterprises from initial risk visibility to automated, remediation-driven action.

Required Qualifications:

• Education: Bachelor’s or Master’s in Computer Science, Engineering, or equivalent practical experience.
• Experience: 8–10 years in data engineering, platform engineering, or customer-facing technical roles.
• Technical Stack:
• Strong programming proficiency in Python, Go, or similar languages.
• Deep experience with REST APIs, webhooks, asynchronous systems, and scalable ingestion pipelines (ETL/ELT).
• Hands-on knowledge of relational, NoSQL, search, or graph databases.
• Domain & Data Expertise:
• Strong foundation in data modeling, normalization, transformation, and correlation.
• Solid understanding of core cybersecurity domains (VM, Cloud Security, IAM, Risk Frameworks).
• Hands-on application of AI or automation to optimize data pipelines and onboarding workflows.
• Soft Skills: Exceptional client-facing, communication, and technical consultation skills.

Preferred Qualifications:

• Background in Forward Deployed Engineering, Solutions Engineering, or Technical Professional Services.
• Familiarity with large-scale, distributed systems and microservices architectures.
• Proven ability to thrive in high-impact, fast-paced, and ambiguous production environments.

Experience Level