Data & MarTech Engineer

AdZeta; United States; On-site; USD 140,000 - 190,000; Full time

Benefits offered by this job

Equity stake in a fast-growing startup

High ownership & autonomy

Generous AI tooling budget

Job summary

AdZeta is building a data and MarTech activation platform that ingests first-party data and pushes predictive signals to major advertising platforms. We are seeking a Data & MarTech Engineer to own activation infrastructure and data pipelines, working closely with our ML team to operationalize models.

This is an early-team role with significant ownership, requiring production Python services, API integrations, and data ingestion across Shopify, CDPs, CRMs, and analytics tools.

Qualifications

• Hands-on experience building production Python applications and APIs.
• Experience integrating with external APIs in production environments.
• Experience with cloud infrastructure (GCP preferred, AWS acceptable).
• Familiarity with retry logic, error handling, and monitoring in live systems.
• Comfortable working in a startup with high ownership and autonomy.
• Willingness to leverage AI-assisted development tools in day-to-day work.

Responsibilities

• Design, build, and maintain outbound integrations with advertising platforms.
• Develop production-grade Python services and APIs.
• Build and maintain event-driven data pipelines.
• Create scalable activation infrastructure for multiple customers.
• Implement monitoring, logging, testing, and observability practices.
• Collaborate with ML engineer to operationalize predictive models.
• Contribute to architecture and platform decisions as an early team member.
• Ship production code quickly while ensuring quality and reliability.

Skills

Python

External APIs

Cloud infrastructure

Retry logic

Observability

Ownership & autonomy

AI-assisted development

Tools

Docker

Terraform

BigQuery

Shopify APIs

GA4

GCP/AWS cloud platforms

Job description

Full-Time | Early-Team, High-Ownership Role

About AdZeta

AdZeta is building the signal infrastructure layer for modern growth teams.

We help brands move beyond ROAS by transforming first‑party customer data into predictive value signals that power smarter bidding and optimization across Google, Meta, TikTok, and other advertising platforms.

Our machine learning engine already exists. What we're building now is the infrastructure that moves those signals where they need to go, reliably, securely, and at scale.

Our architecture is extensively documented and our ML engine is live. You'll be working directly with our founding ML engineer, Dr. Yannik Pitcan (PhD Statistics, UC Berkeley; ex-Google), to bring the product to market.

The Opportunity

We are looking for a Data & MarTech Engineer to own the activation and data infrastructure that powers our platform.

This is not a traditional Data Engineering role and it is not a Machine Learning role.

You will be responsible for building the systems that move customer data into our platform and deliver predictive value signals out to advertising platforms through server‑side APIs.

What You'll Build

Activation Infrastructure

Build and maintain the Python services that dispatch predictive customer value signals into:

• Google Ads Offline Conversion Import
• Meta Conversions API (CAPI)
• TikTok Events API

You'll own:

• Payload formatting to each platform's specific schema and hashing requirements
• Authentication management (OAuth token lifecycle, server‑to‑server headless auth)
• Retry logic with exponential backoff and dead‑letter queue handling
• Error handling and event delivery monitoring
• API integrations with platform‑specific identifier, consent, and time window constraints
• Reliability, observability, and crash‑safe idempotency

Data Ingestion

Build systems that ingest and process first‑party customer data from:

• Shopify
• Customer Data Platforms (CDPs)
• CRM systems
• Product analytics platforms
• Event tracking systems

You'll create reliable pipelines that move data into BigQuery and support downstream activation workflows.

Infrastructure & Automation

Help build and maintain:

• Multi‑tenant cloud infrastructure
• Infrastructure as Code
• Containerized services
• Monitoring and alerting systems

Responsibilities

• Design, build, and maintain outbound integrations with advertising platforms
• Develop production‑grade Python services and APIs
• Build and maintain event‑driven data pipelines
• Create scalable activation infrastructure that supports multiple customers
• Implement monitoring, logging, testing, and observability best practices
• Collaborate closely with our ML engineer to operationalize predictive models
• Contribute to architecture and platform decisions as an early team member
• Ship production code quickly while maintaining quality and reliability

Required Qualifications

• Hands‑on experience with at least one advertising platform API (Google Ads Offline Conversion Import, Meta Conversions API, TikTok Events API) or equivalent server‑side conversion tracking.
• Professional experience building production applications in Python
• Experience integrating with external APIs in production environments
• Experience working with cloud infrastructure (GCP preferred, AWS acceptable)
• Experience building systems with retry logic, error handling, and monitoring
• Comfortable working in a startup environment with high ownership and autonomy
• Comfortable with AI‑assisted development workflows (Claude Code, Cursor, Copilot) — we provide generous tooling budgets and expect engineers to leverage AI to accelerate development

Preferred Qualifications

• Experience with Shopify APIs and webhooks
• Terraform or Infrastructure as Code experience
• BigQuery experience
• Event‑driven architectures and message queues
• Docker and containerized deployments
• Identity resolution, customer matching, or audience activation experience

Core: Python, SQL, Docker

Data: BigQuery, Shopify, GA4, Customer Data Platforms

AdTech: Meta Conversions API, Google Ads API, TikTok Events API, Server‑Side Tracking

Why Join AdZeta

• Early‑team role with meaningful ownership and significant equity
• Work directly with the CEO and founding ML engineer (PhD Statistics, UC Berkeley; ex-Google)
• Unusually well‑documented architecture — you won't be guessing what to build. The blueprint exists; your job is to implement it with production quality.
• Significant equity upside
• High‑impact work with immediate customer visibility
• Fast‑moving, execution‑focused culture
• Generous AI development tooling budget (Claude Code, Cursor, Copilot)