Schema and Identity Resolution strategy the canonical event schema, naming conventions, versioning policy, and the identity-resolution model (anonymous/identified user merge, cross-device, cross-surface, edge-cookie strategy).
You define the standards; Engineering and Product implement to them.
Edge and Server-side conversion architecture the system that reduces reliance on browser-side tracking.
You design and own the flow where events are captured at the edge (Cloudflare Workers, server-side CDP sources) and delivered server-to-server to ad platforms via Meta CAPI, Google Enhanced Conversions, and TikTok Events API, with deduplication against any browser-side mirror.
First-party data moat a measurement foundation that does not depend on third-party cookies, pixels, or ad-blocker-vulnerable tags.
Our CAC, attribution, and experiment readouts rest on signals you own end-to-end.
Tie-breaker for data integrity when three tools disagree on conversion rate, your definition wins.
You own the reconciliation model, the single source of truth for conversion and CAC, and the authority to say this is the number in an exec review.
Customer Data Platform Segment SDKs on web and mobile surfaces, server-side sources, destination routing, and identity stitching.
You set the standard for what an event must contain; Engineering implements to it.
You are the registrar and governance owner, not a report author.
Conversion APIs, end-to-end not just configured endpoints.
Event enrichment in the warehouse, reverse-ETL out, dedup with any client-side mirror, match-rate monitoring, EMQ optimisation, and continuous improvement of ad-platform signal quality.
Experiment design and measurement feature-flag-driven A/B tests for onboarding, checkout, and pricing flows with clearly defined primary metric, guardrails, sample-size planning, and SRM / peeking discipline.
Pipeline reliability and incident response detect, triage, and resolve tracking outages (identity-resolution breaks, event drops, pixel misfires, CAPI deliverability regressions).
Cross-functional alignment running the weekly analytics sync with engineering, data, and growth; unblocking teams by owning the governance decisions no one else can make.
Required Experience
8 to 12 years in MarTech engineering, product analytics, or growth engineering at consumer-facing digital businesses ideally including at least one direct-to-consumer, e-commerce, or subscription product.
Proven track record designing Edge-based and Server-side conversion architectures not just configuring CAPI endpoints, but reasoning about where in the stack each event should originate to maximise match rate, perform reliably as browser-tracking signals evolve (ITP, ATT, ad blockers), and produce a trustworthy CAC signal.
Hands-on experience with event deduplication across client, server, and edge sources is required.
Deep, hands-on experience designing and governing event tracking plans across web and server-side events you have authored the schema that other engineers implement to, run schema reviews, and held the line on data-quality standards when under delivery pressure.
Identity resolution design anonymous identified user merge, cross-device and cross-surface stitching, edge-cookie strategies for ITP-resistant first-party identity.
You have designed this, not just consumed it.
Production experience with Segment (or equivalent CDP such as RudderStack or mParticle) SDK integration, server-side sources, destinations, and debugging at the event level.
Production experience with Mixpanel (or Amplitude, Heap, or equivalent) including event registry governance, funnels, cohorts, and diagnosing data-quality issues end-to-end.
Hands-on production experience with at least one server-to-server conversion pipeline: Meta Conversions API, Google Enhanced Conversions, TikTok Events API, or equivalent including EMQ / match-rate tuning and dedup design.
Hands-on configuring reverse ETL syncs from the warehouse to ad platforms (Polytomic, Hightouch, or Census) mapping fields to destination payloads, debugging failed syncs, and managing audience sync cadence.
You configure and operate these tools; warehouse modeling sits with Data Engineering.
Comfortable writing ad-hoc SQL against BigQuery, Snowflake, or Redshift to validate event data, reconcile numbers between analytics tools, and build audience definitions working with existing warehouse models rather than building them.
Comfortable reading and writing JavaScript/TypeScript for SDK integration, tag implementation, and edge workers.
Proven track record running experiments end-to-end hypothesis, feature flag, instrumentation, measurement, readout including awareness of statistical gotchas (sample-ratio mismatch, peeking, sequential testing).
Experience operating during a platform migration, re-platforming, or a major tracking overhaul you have lived the ambiguity and can bring order to it.
Tools And Technologies
The following stack describes what you will work with day-to-day.
You do not need hands-on experience with every single tool depth in 6070% of this list is what we are looking for, along with the pattern-matching to learn the rest quickly.
