This deck is for Principal Backend Engineer (Go) at FunnelStory Job description

Quoted from the posting on Wellfound. Remote (India), full time.

What are we building?

FunnelStory is building the GTM context graph — the infrastructure layer that lets revenue teams and AI agents reason over live customer intelligence. We fuse GTM data from many systems, maintain durable account truth, and power permissioned actions and agents on top.


Backend: Go, PostgreSQL, Docker, AWS; enterprise and on-prem deployments.

Opportunity

  • Own major backend work across the data platform (connections, ETL/reverse ETL, sync) and the agent layer (workflow runtime, reliability, usage/billing)
  • Solve hard product problems — incremental refreshes, reconciliation, integrations at scale
  • Shape architecture, engineering process, and culture with the founders
  • Work closely with the team to design and ship new features
  • Mentor others and help lead as we grow

Who are you?

  • Opinionated, accountable, and ownership-driven
  • You tackle unknown problems and find practical solutions; you communicate clearly
  • You challenge weak ideas and care about products customers can trust
  • You use AI tooling to ship faster — and you still write clean, reliable code (not slop)

Your experience

  • 7+ years as a senior/principal backend engineer
  • 4+ years building services and REST APIs in Go
  • 4+ years of SQL with PostgreSQL (or similar)
  • Docker and AWS
  • Experience with data pipelines, integrations, or AI/agent systems in production
  • You have designed or owned major backend modules/components

What we offer

  • A small, senior, passionate team
  • Competitive salary and equity
  • Flexible vacation policy
  • Remote (India)

Skills

SQL Artificial Intelligence REST APIs Docker Go (Golang) AWS Docker / Docker Compose / Kubernetes LLMs Agentic AI

A simple principle I follow is

What Customer's really want?

More of

  • Love
  • Money
  • Acceptance
  • Free time

Less of

  • Stress
  • Conflict
  • Hassle
  • Uncertainty

Let's address the problem statement for a B2B business's post sales customer journey.


What impacts the revenue is the renewal and the churn of user accounts.


The metrics that teams track to define the success criteria are often lagging indicators. These indicators are tracked after the event occurs (renew or churn), and they drive reactive actions.


To be proactive, what we should track is leading indicators. Leading indicators are hard to track. They are scattered across many data sources, structured and unstructured. They are not always deterministic, for example customer sentiment. They are also missing context and relationships, both with one another and with the lagging indicators.


Moreover, there are CSMs, CSAs and account executives working in silos, each managing their own accounts. They often miss out on 2 types of context:

  • Common indicators that are relevant to all accounts, or to a group of accounts by type, region, industry, and so on.
  • Definitions for the same indicator that change from account to account, even though the accounts overlap on type, region or industry, and even though some of them share the same parent account.
Leading indicatorsLagging indicatorsAccount actionProactiveReactiveMissing, and hard to trackConversation signalsPricing concernCompetitor mentionedChampion tone shiftRelationship signalsSingle threadingChampion departureWent darkProduct usage signalsUsage dropStalled adoptionSeat utilisationExpansion signalsSeat limit approachedSecurity requestHealth scoreSupport escalationsCSAT and NPSRenewal forecastRenewExpandDowngradeChurn
Alice, CSMBob, CSAAcmeCarol, CSMDan, CSAGlobexInitechErin, CSMFrank, CSAUmbrella
Atlan's User Journey
StrategyData Estate Onboarding(Catalog and Lineage)My primary ownershipImplementationand AdoptionMy secondary ownershipMeasure and Scale(Value)Renew and ExpandThe loop closes, and the next contract starts here

Strategy

The contract sets the scope. Which data sources to ingest metadata from, and what to implement on top: lineage, tags, glossary, data products, data quality, and governance workflows.

Data Estate Onboarding (Catalog and Lineage)

The team creates connections to the data sources. Workflows then ingest metadata and lineage. A workflow runs on a schedule, or on demand, to keep the data in sync.

Implementation and Adoption

Once the metadata and lineage land, the customer data teams implement the downstream use cases and adopt them.

Measure and Scale (Value)

Business users drive the use cases and collaborate with Atlan as the source of truth and the context layer.

Renew and Expand

The contract renews or churns on value and viability. Expansion adds breadth, which is more data sources, or depth, for example data quality added after a successful lineage rollout.

Leading indicatorsLagging indicatorsAccount actionAdoption depthGlossary and tag usageData product adoptionWorkflow healthWorkflow run success rateConnection sync failuresMetadata and lineage qualityQuery parsing success rateLineage coverageCustomer happinessWorkflow resiliencySupport ticketsEscalationsTime to resolveRenewExpandDowngradeChurn
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