TE - 1458 Staff MarTech Engineer

Softobiz; Hyderabad; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

Softobiz is seeking a Staff MarTech Engineer to own the end-to-end marketing technology, product‑analytics stack, and data integrity. You will define standards for event schemas, identity resolution, and governance that feed CAC reports used across the business.

You will build edge-based and server-side conversion architectures, partner with engineering, data, growth and product teams, and ensure accurate measurement from first click through to advertising platforms.

Qualifications

• 8–12 years in MarTech engineering, product analytics, or growth engineering for consumer digital platforms.
• Hands‑on experience with edge-based and server-side conversion architectures to maximize match rate.
• Proven ability to design and govern event tracking plans across web and server‑side environments.
• Identity resolution design including cross‑device stitching and edge‑cookie strategies.
• Production experience with Segment, Mixpanel, and server‑to‑server conversion pipelines.

Responsibilities

• Own governance and truth of data across the marketing tech stack.
• Define canonical event schema, identity resolution standards, and data contracts.
• Design and own edge-based and server-side conversion workflows to ad platforms.
• Collaborate with Engineering, Data, Growth, and Product to align metrics and trust.
• Lead reconciliation models for CAC and conversion readouts.

Skills

Data governance

Identity resolution

Ownership mindset

Cross-functional collaboration

Experiment design

Tools

Segment

Mixpanel

Edge & server-side architecture

Meta CAPI / Google Enhanced Conversions

Cloudflare Workers

Job description

Job Title: MarTech Engineer

Location: Hyderabad, India

Work Model: Full-time

Time Zone: Must overlap with US working hours

Experience Required: 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

Below is the stack you will work with day‑to‑day. Depth in 60–70% of this list is what we are looking for, along with the pattern‑matching to learn the rest quickly.

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

Nice to Have

• Healthcare, fintech, or other privacy‑sensitive domain experience — designing tracking that respects PII / PHI boundaries and withstands compliance review.
• Experience as the de‑facto data‑governance authority at a growth‑stage company — the person who owns the tracking plan, the metric catalog, and the definitional authority for ’what counts as a conversion.’
• Production experience with Cloudflare Workers, Durable Objects, Workers KV, or Workers Analytics Engine for edge‑based tracking and identity.
• Experience retiring legacy analytics tooling — for example consolidating tag managers, migrating session‑replay vendors, or moving experimentation into a native platform.
• Background in privacy / consent engineering: iOS ATT, consent management platforms, GDPR / DPDP compliance.
• Familiarity with dbt and the modern data stack — enough to partner with Data Engineering as a peer, not a client.
• Prior early‑MarTech‑hire experience at a growth‑stage product company.

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.

Softobiz is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. All qualified applicants will be afforded equal employment opportunities without discrimination based on race, creed, color, national origin, sex, age, disability, or marital status.