What You'll Own :

- 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.

- Product analytics Mixpanel event registry hygiene, funnel / retention / cohort reports, session replay, and experimentation.

- 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.

Category :

Tools :

- Customer Data Platform

- Segment (required), identity-stitching / user-unification layer, server-side sources

- Product Analytics

- Mixpanel (required), event registry, Session Replay, Experiments 2.0

- Edge & Server-side Architecture

- Cloudflare Workers, Cloudflare Zaraz, Google Tag Gateway, server-side tagging

- Conversion APIs

- Meta Conversions API (CAPI), Google Enhanced Conversions, TikTok Events API

- Reverse-ETL (configure & operate)

- Polytomic (preferred), Hightouch, Census

- Data Warehouse (consumer)

- BigQuery, Snowflake, or Redshift ad-hoc SQL against existing models

- BI/Visualization

- Omni, Looker, Mode, Metabase

- Experimentation

- Mixpanel Experiments 2.0, LaunchDarkly, Statsig, Optimizely, VWO Session Replay

- Mixpanel Session Replay, PostHog, FullStory

Ad Platforms :

- Meta Ads Manager, Google Ads, TikTok Ads

Languages :

- JavaScript/TypeScript, SQL; Python is a plus

- Governance & Schema

- Segment Protocols, Mixpanel Lexicon, data contracts, PII / consent policy

Collaboration :

- Linear, Slack, Google Docs & Sheets, Confluence / Notion

How You Work :

- You treat the tracking layer as a product versioned, documented, reviewed, with clear ownership.

- You see CAC, CVR, and retention numbers as contracts, not reports.

- When a number changes, you know whether it is signal or system.

- You are as comfortable debugging a dropped event in an edge worker as you are explaining an attribution model to senior leadership.

- When there is ambiguity about what a metric means, you resolve it definitively rather than passing it around.

- Definitional authority is part of the job.

- You prefer one integrated tool over three bolted-together ones, but you are pragmatic about migrations and their messy middles.

- You partner with engineering rather than throwing tickets over the wall you can read the code that emits the event you are trying to measure.

- You bring clarity to ambiguous data you can tell the difference between a real regression and a phantom caused by a system being turned off.

- You write things down.

- The team should not need to re-learn a decision you have already made