Our Client Currently looking for Staff MarTech Engineer
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
– 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, 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.
Are you interested in this position?
Apply by clicking on the “Apply Now” button below!
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