Job Title: MarTech Engineer
Location: Hyderabad, India
Work Model: Full-time
Time Zone: Must overlap with US working hours
Experience: 8 – 12 years
Staff MarTech Engineer
Product Analytics, Growth Platform & Data Integrity
About the Role
We are looking for a Staff MarTech Engineer to own our end-to-end marketing technology and product-analytics stack. You will be the single owner for how events flow from a user's first click, through onboarding and checkout, into our data warehouse, our product-analytics tooling, and the advertising platforms that drive acquisition.
This is a hands-on individual-contributor role. You will partner daily with engineering, data, growth, and product teams. Success looks like: every conversion captured, every experiment measurable, every advertising dollar getting accurate feedback, and a measurement layer that the whole organisation trusts.
Scope note: this role works closely with the Data Engineering function but does not own warehouse architecture, pipelines, or data modeling. You consume the warehouse — the Data Engineering team builds and maintains it.
Why this role exists — the architecture mandate
To strengthen attribution and protect CAC signal quality as the business scales, we are investing in system-level design rather than incremental tool configuration.
Specifically: an Edge-based and Server-side conversion architecture (Meta Conversions API, Google Enhanced Conversions, TikTok Events API, Cloudflare Workers at the edge) engineered to perform reliably as browser-side tracking continues to evolve, and to keep CAC measurement accurate across ad platforms.
The outcome is a first-party data moat — the foundation that lets every ad-spend decision, experiment readout, and growth bet rest on data the organisation trusts.
What ownership means at the Staff IC level
Ownership in this role means Governance and Truth. You are not a ticket-taker for marketing requests.
You are the single tie-breaker for data integrity. You own the Schema and the Identity Resolution strategy — you define the standards that Engineering and Product must follow so that when the business looks at a CAC report, everyone in the building trusts the number.
You own the Why behind the data, not just the How of the pipeline.
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
- 8–12 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 60–70% 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.
