MarTech Engineer

Softobiz

Hyderabad

On-site

INR 3,000,000 - 6,000,000

Full time

14 days+

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Job summary

Softobiz is seeking aStaff MarTech Engineer to own our end-to-end marketing tech and product analytics stack. You’ll govern events from first click through onboarding, into the data warehouse and ad platforms, establishing a single truth for CAC and conversions.

You will partner with engineering, data, growth, and product teams to ensure every conversion is captured, every experiment measurable, and advertising spend feeds trusted insights across the organization.

Qualifications

8–12 years in MarTech engineering, product analytics, or growth engineering at consumer-facing digital businesses.
Deep, hands-on experience designing and governing event tracking plans across web and server-side events.
Production experience with Segment (or equivalent CDP) and with Mixpanel/Amplitude.
Hands-on experience with server-to-server conversion pipelines (CAPI, Enhanced Conversions, Events API).
Experience configuring reverse-ETL syncs from the warehouse to ad platforms.
Proficient in SQL against BigQuery, Snowflake, or Redshift for validation and audience definitions.
Proven track record running experiments end-to-end with proper statistical discipline.
Experience during platform migrations or major tracking overhauls.

Responsibilities

Own governance and truth for data integrity and CAC reporting.
Oversee Customer Data Platform design, identity stitching, and event schemas.
Govern product analytics, including funnels, cohorts, and experiments governance.
Lead end-to-end conversion APIs and data enrichment in data warehouse.
Design and measure experiments with clear primary metrics and guardrails.
Ensure pipeline reliability and rapid incident response for tracking issues.
Facilitate cross-functional alignment and decision governance with engineering, data, and growth teams.

Tools

Segment

Mixpanel

Amplitude

Heap

Meta Conversions API

Google Enhanced Conversions

TikTok Events API

Polytomic

Hightouch

Census

BigQuery

Snowflake

Redshift

Omni

Looker

Mode

Metabase

LaunchDarkly

Statsig

Optimizely

VWO

Session Replay

Meta Ads Manager

Google Ads

TikTok Ads

Job description

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.

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

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.
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.
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.
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.