AirOps

Sales Development Rep

• Build the Engine: develop and execute creative outreach strategies
• Be an AI Matchmaker: conduct discovery calls as deep-dive consultations
• Own the First Impression: build authentic relationships with key decision-makers
• Pass the Torch: work with Account Executives to ensure seamless handoffs
• The "Data Artist": keep CRM clean and organized for data-driven iteration
• Startup Speed: thrive in a high-growth environment and agility is key.

Sales Development Representative, German Speaking

Data Scientist

• Design and deploy end-to-end machine learning systems including NLP models, search and recommendation algorithms, and LLM-based applications.
• Build ML systems that analyze AI search behavior, identify content opportunities, and predict performance across different AI-driven platforms. Create algorithms that help brands understand and optimize for how AI agents discover and rank content.
• Collaborate with product managers to translate business requirements into technical solutions.

Engineering Manager

• Run the Integration Specialist team: you set the bar, run the 1:1s, build the leveling rubric, and stand behind every commit the team makes. People grow here or you change something
• Team throughput as a real metric: you forecast capacity, manage queue depth, and drive integrations-per-specialist-per-quarter up quarter after quarter. The systems that get you there (tooling, templates, SOPs) are yours to build
• Integration delivery end to end: scoping, build, QA, handoff. You translate customer and partner needs into clean technical scope and represent integration realities — rate limits, deprecation risk, partner constraints — in every design review
• The integration portfolio as an ongoing business asset: usage, error rates, partner SLAs, incident response. You make the maintenance, deprecation, and version-upgrade calls with a clear cost/benefit view, not by default
• Partner relationships that matter: top integration partners run through you. Technical escalations, co-build conversations, co-marketing — you are the credible point of contact and you bring opportunities back for the commercial team to act on
• Integration-led deals with Sales, CS, and Product: feasibility calls, contract language, rollout, retention. When integrations are the thing standing between us and a deal, you are in the room and you have a point of view

Engineering Manager, Integrations

Platform Engineer

• The deployment pipeline end to end: CI/CD from commit to production, including staging build gating, automated rollback, and zero-manual-step deploys
• Environment parity across dev, staging, and production: DB initialization, auth configs, integration consistency, and review apps that actually reflect reality
• Incident reduction infrastructure: app-level health checks, migration safeguards, automated alerting on deploy failures, and deployment guardrails that prevent the class of outages we've had
• Observability that produces signal, not noise: fixing false-positive exceptions, making request time metrics useful, and cleaning up tech debt in service naming
• Agentic coding integration: evaluating and deploying AI-assisted developer tooling (automated bug trackers, code review automation, environment provisioning) to accelerate engineering productivity
• ECS operations and infrastructure management: running tasks, container orchestration, RDS, and new environment setup

Data Engineer

• Reliability and completeness of our multi-provider ingestion pipeline: build failover logic, track daily completion rates per AI provider, and maintain the systems that ensure customers always get accurate, timely data
• Data quality and consistency end to end: own the contracts between ingestion, processing, and downstream serving so that what customers see in the product is correct and trustworthy
• Extraction pipeline performance: design and maintain async processing patterns that keep our ingestion throughput ahead of demand as we scale
• Data readiness for new features: partner with engineering upstream so instrumentation, schema changes, and pipeline capacity are in place before features ship
• Pipeline observability: build and maintain health monitoring across the full data stack so the team knows the state of the system in real time

Sales Development Representative, German Speaking

Data Scientist

Engineering Manager, Integrations

Platform Engineer

Senior / Staff Data Engineer