Marketing Technology Engineer

“As our Marketing Technology Engineer, you’ll do more than keep our data platform humming — you’ll build the AI applications and agents that put that data to work, automating how our marketing and product teams operate. You’ll own the full path from a mature BigQuery and dbt foundation to deployed, secure, production-grade tools that give every team leverage. If you’re excited to engineer at the intersection of data, AI, and marketing — and to do it with real craftsmanship — come help us redefine the landscape of e-learning solutions.” — Saravana Sivanandham, CMO

Absorb Software is looking for a Marketing Technology Engineer to own and evolve the technology behind our data-driven and AI-driven marketing. This is a dual mandate. On one side, you’ll steward a mature data platform — a BigQuery warehouse with dbt-based transformations — ensuring clean, reliable, well-governed data. On the other, you’ll design, build, and deploy applications and AI agents on Google Cloud that automate marketing and product work and give our teams self-serve leverage.

Throughout, you’ll set the engineering bar. Much of what you build will be created with AI assistance, and your job is to make sure that code is held to professional standards: version-controlled, reviewed, tested, securely deployed, and maintainable. This is a senior individual-contributor role that provides technical leadership and architectural direction across our marketing technology stack. It does not have direct reports.

What you’ll do:

• Own and evolve our marketing data platform — a BigQuery data warehouse with dbt-based transformations — designing and maintaining ETL/ELT pipelines that connect diverse data sources and scale as our measurement needs grow.

• Design, build, and deploy applications and AI agents on Google Cloud — exposing backends for agentic use and lightweight frontends/UIs where needed — to automate marketing and product workflows.

• Develop AI-powered tools that let Marketing, Product, and other teams self-serve automation, and apply AI to automate data-engineering work itself.

• Uphold engineering best practices across an AI-assisted codebase: Git-based version control, code review, semantic versioning, automated testing, and CI/CD for clean, reproducible deployments. You are the quality gate that ensures AI-generated code is production-ready — not shipped as-is.

• Own data security and governance across the platform: least-privilege access (IAM), secret management, PII handling, BigQuery row/column-level security, and guardrails on AI systems that have access to data (e.g., against prompt injection and data exfiltration).