Data / Platform Engineer

About aematic

aematic is the optimization and measurement layer for advertising on AI answer-engines, starting with OpenAI’s ChatGPT. As advertising opens up on AI assistants, advertisers need real tooling to plan, launch, optimize, and measure campaigns on a channel the native consoles barely support. aematic runs the full loop for the advertiser: research the account, plan and launch campaigns, manage bids, and measure outcomes. Everything we do depends on moving advertising data reliably and at scale.

The role

You build the data foundation the whole company runs on: the pipelines that sync ad-spend, conversion, and performance data; the warehouse and models the bidding engine and measurement science depend on; and the infrastructure that keeps it reliable. You are the person who makes that data trustworthy, timely, and usable. This is a high-leverage role with broad ownership across data engineering and platform.

What you will do

• Build and operate data pipelines that ingest and sync data from ad-platform APIs, conversion sources, and customer systems.
• Design the data models and warehouse that power bidding, measurement, and customer reporting.
• Ensure data quality, freshness, and observability; downstream optimization and measurement are only as good as this layer.
• Build the infrastructure and tooling that lets ML engineers and data scientists work fast.
• Own deployment, monitoring, and reliability for the data platform as spend under management grows.
• Partner closely with backend, ML, and data science across the team.

What we are looking for

• 4+ years in data engineering, platform, or infrastructure roles, with strong Python and SQL.
• Experience building production data pipelines and warehouse/lakehouse models.
• Comfort with cloud infrastructure, orchestration, and the operational side of keeping data systems reliable.
• A pragmatic, ownership-driven approach in an environment where you build the foundation, not inherit it.
• Care for data quality and observability; you make data others can trust.

Nice to have

• Experience with advertising, analytics, or other high-volume event data.
• Familiarity with the infrastructure behind ML or measurement systems.

Why now

The foundation you build now determines how fast the optimizer and the measurement layer can improve as the company grows.