Softobiz - 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 (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 (ref:hirist.tech)