Marketing Analytics Engineer
$155–$185,000 year
HybridNew York City, New York, United States
Job Summary
Translate data needs from finance, marketing, and product teams into technical requirements for centralized reporting. Define, build, and manage key data pipelines, foundational data products, dashboards, and tools to enable self-serve analytics across the company. Own attribution and conversion measurement end-to-end, including server-side tagging, conversion APIs, and audience sync across paid channels and payment providers. Maintain marketing integrations and support consent and compliance tooling to ensure privacy-safe standards. Manage marketing website operations, tracking setup, and technical support for SEO/AEO/GEO initiatives. Explore automation systems to improve brand visibility in search and AI-generated answers. Work cross-functionally with engineering, marketing, and finance to translate between technical and business needs. Based in NYC with a hybrid schedule requiring two office days per week.
Required Qualifications
- 3+ years of experience in analytics engineering, marketing engineering, data engineering, or a similar technical role with a marketing-facing component.
- Expertise in SQL and JavaScript/Node to transform data into accurate, clean data models.
- Hands-on experience with GA4, Google Tag Manager (including server-side), and BigQuery, implementing them, not just reporting.
- Experience with attribution and conversion tracking for paid channels (conversion APIs, audience sync, event deduplication).
- Familiarity with marketing and analytics tooling (i.e., Amplitude, Customer.io, Impact, AppsFlyer, etc.) or similar.
- Strong communicator who can work across engineering, marketing, and finance, translating between them as a core part of the job.
- Team player who can manage time independently.
- Based in NYC.
Desired Qualifications
- Bachelor's degree in Computer Science or equivalent.
- Swift
- Familiarity with cloud data environments (AWS, GCP, or Azure) and columnar/analytical databases.
- Experience working with AI agents, LLM-powered tooling, or similar systems.
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