Director of Technical Support Engineering

Responsibilities

You will lead and scale the technical support engineering function, building AI-native systems to automate diagnostics and improve customer outcomes. You will also manage end-to-end support operations, including incident response, team coaching, and deep collaboration with product and engineering teams.

Requirements

The role requires a proven track record of building and transforming technical support teams with hands-on experience in SQL, APIs, and cloud data systems. Candidates must demonstrate expertise in designing AI-enabled support workflows and managing complex customer escalations under pressure.

Skills

• Technical support leadership
• AI-native systems
• SQL
• APIs
• Debugging
• Cloud data systems
• Capacity planning
• SLA management
• Incident response
• Team building
• Automation
• Knowledge management
• Cross-functional collaboration
• Data syncs
• Authentication
• Troubleshooting

Benefits

• Medical coverage
• Dental coverage
• Vision coverage
• Life insurance
• Disability coverage
• 401k
• RRSP
• Flexible time off
• Recharge days
• Home office stipend
• Professional development stipend
• Equity

About this role

Director of Technical Support Engineering
Customer Solutions Group | North America
Come help us reimagine GrowthLoop’s technical support function from the ground up. You’ll own the customer support experience end to end, from the moment an issue comes in to the moment the customer confirms it’s fixed. You’ll build and coach a strong team of technical support engineers, and you’ll design AI native systems that get smarter the more they’re used.
You’ll report directly to the VP of the Customer Solutions Group and lead the Technical Support function within CSG, working closely with Product and Engineering. This is a hands-on leadership role. You’ll set the direction, and you’ll also dig into the hardest issues yourself, build tooling, and coach your team through the work.
What You Will Do
Build and Lead Technical Support Engineering
Recruit, develop and lead technical support engineers who can investigate and solve problems independently. Set a high bar for SQL, APIs, debugging and cloud data systems. Stay hands-on in complex cases.
Build a distributed team, with initial expansion focused on India. Use workload, comparable industry benchmarks and unit economics to recommend staffing, hiring models and sustainable coverage while retaining ownership of quality and customer outcomes.
Build an AI Native Support Organization
Build AI-assisted intake, triage, investigation and diagnostics connected to approved knowledge, logs, telemetry and authorized customer context. Enable AI agents to manage workflows, maintain knowledge and propose improvements, working toward safe, self-healing support systems.
Evaluate against real support cases before deployment. Define permissions, privacy controls, human approval for system changes and clear escalation to human incident response. Expand automation when it improves verified resolution, quality and productivity.
Own the End to End Support Experience
Own intake, prioritization, technical investigation, escalation, resolution and customer communication. Establish clear L1, L2 and Engineering handoffs, and define boundaries with Services, and Customer Architects for implementation and advisory requests. Ensure every issue has a clear owner and customers retain continuity through each handoff.
Own service levels against customer commitments, including reliable 24/7 critical incident response. Build sustainable on-call rotations across Support and Engineering with clear severity thresholds, escalation paths, handoffs, backup coverage and recovery time after overnight incidents.
Track response, resolution, SLA attainment and team workload so coverage meets customer needs without depending on any individual’s continuous availability.
Build Knowledge Tooling and Automation
Overhaul customer documentation and internal knowledge as part of the support system. Manage knowledge sources and runbooks in GitHub, own review and publishing standards, and validate workflows with Product and Engineering before guidance is published.
Turn investigations into reusable knowledge, diagnostic scripts and tools. Improve visibility into customer workloads, integrations and infrastructure so recurring issues become easier to diagnose or are eliminated. Make documentation and automation part of the team's daily work.
Partner Deeply with Engineering and Product
Partner with Engineering to establish shared escalation ownership, response expectations and a clear path for stalled issues. Reduce unnecessary Engineering interruptions through independent investigation and reproducible escalations, while maintaining joint accountability for incident response, root cause analysis and permanent fixes.
Bring support trends by product area into customer and product discussions. Route internal technical questions through a clear support path, validate answers, and partner on reliability, observability, diagnosability and customer self-service.
What You Bring
Required Experience
A proven record of building or transforming technical support, hiring and coaching distributed technical support engineers, and owning customer outcomes end to end while staying active in investigations.
Hands-on troubleshooting across SQL, APIs, authentication, schemas, data syncs and exports, plus evidence of building diagnostic scripts, tools or automation that improved support.
Experience designing and evaluating AI-enabled support systems, including grounded answers, permissions, failure analysis and human handoffs. Show what improved and how you controlled risk.
Experience turning investigations into reliable, version-controlled knowledge and runbooks, with clear review and publishing ownership.
Strong customer judgment under pressure. Prioritize issues using customer impact, severity and contractual commitments, including when the cause or scope is unclear. Communicate uncertainty honestly, set clear expectations and work across teams to move stalled issues toward resolution.
Strong judgment in capacity planning, comparable benchmarking and support economics, plus clear customer communication and effective partnership with Engineering, Product and customer-facing teams.
Nice to Have
Experience supporting enterprise data platforms, cloud warehouses or marketing technology, including audience activation and destination integrations.
Experience scaling an India-based technical team, implementing Knowledge-Centered Service or safely expanding an internal AI assistant into customer-facing support.
About GrowthLoop
GrowthLoop built the first composable CDP, helping marketers leverage live, first-party data from their cloud warehouse to launch highly targeted campaigns. That composable foundation gave way to the Compound Marketing Engine, an evolution of the composable CDP that drives compound growth by using agentic AI to accelerate the marketing cycle.
Now, we’re building what comes next: an agentic system that uses causality data to find the actions that actually improve customer outcomes and gets smarter with every c...

About GrowthLoop

Software Development · 51-200 employees · headquartered in New York City, New York · founded 2015