Revenue Operations and AI Systems Engineer

About Datahash

Datahash is a privacy-centric first-party data platform. Our core product, Datahash Signals, gives marketers low-code, no-code integrations between web, app, e-commerce, CRM, marketing automation and data warehouses on one side and the major ad channels on the other: Meta, Google, TikTok, Snap, Amazon and more. We are one of a handful of badged integration partners across these channels and work with them directly on first-party data adoption programmes, where advertisers are moving to server-side integrations, conversions APIs and privacy-safe measurement. We are bootstrapped, profitable, and run across Dubai, Bangalore, Mumbai and London with clients in EMEA, India, Africa, APAC and the US.

The role

You build and run the revenue system: the engine tags on CRM records that make revenue measurable by engine and gate, the dashboards, the renewal and dunning automation, the quota models that must exist before any offer is made, and the growing set of agents that carry routine growth and account-management work. Half your job is revenue operations; half is building agents on our Claude and Zoho stack, with a catalogue of skills already written and a plan for which agents come first. If only one hire happened this year it would be this one.

What you will do

1. Instrument revenue. Engine tags on every deal within two weeks of joining; the revenue board consistent between tiles and tables; geography backfill; the deal-valuation and pace rules encoded, not remembered.
2. Automate the money loop. Renewal deals spawned at T-90, dunning and overage alerts live inside six weeks, payment failures down by half in your first quarter.
3. Model before promising. Quota-versus-capacity models for every seat before an offer is made; per-seat lift reports.
4. Build the agents. Own the agent build queue across growth and account management: skills, eval sets, critics, decision records, acceptance telemetry, on the Agent SDK host with the CTO.
5. Keep the spine clean. CRM hygiene as a nightly automated sweep, not a quarterly cleanup; duplicate proposals, stale dates, deactivated owners, all surfaced and fixed.

What success looks like

• Engine tag live in two weeks, dunning automation in six, payment failures down 50 percent in the first quarter.
• Every quota offered in the year was modelled first.
• Agent acceptance ledger live and read weekly by every function owner.

How we hire

We move fast and we are transparent about how we decide. An AI assistant helps us screen applications against the must-haves in this description and helps schedule conversations; every decision about a person is made by a person.

Step 1: a short structured screen within two working days of applying. Step 2: a 50-minute conversation with the hiring manager. Step 3: a small case exercise close to the real work, which you present to two or three of us. Step 4: fit and terms with our VP Delivery or Head of Sales and Partnerships, and for senior roles the founder. We aim to give you a decision within five working days of the final conversation.

We evaluate evidence rather than claims, the quality of your questions, how you handle ambiguity, and how naturally you already use AI in your own work.

Requirements

What we need from you

• Four to five years in revenue operations, sales operations, business systems or analytics engineering in a B2B software company.
• Hands-on with a CRM as a builder (Zoho preferred; Salesforce or HubSpot fine), with workflows, functions, webhooks and reporting.
• You write code: Python or JavaScript, SQL, APIs; you have shipped automations that other people relied on.
• You have built with LLM tooling (Claude, agent SDKs, MCP or similar) beyond chat, and can explain what you would never let an agent do.
• Rigour with data: you distinguish gross from net, subscription from invoice, and you check both directions before you trust a number.

Nice to have

• Zoho One depth: CRM, Books, Billing, Desk, Flow, Analytics.
• Experience in a partner-led or usage-billed business.
• Experience designing evals or QA for AI systems.

Probably not a fit if

• You want to be an analyst producing reports; this seat builds systems that produce them.
• You are uncomfortable owning production automation with a kill switch.

Benefits

• A startup environment backed by a stable, bootstrapped, profitable business.
• Competitive compensation and one of the most employee-friendly ESOP programmes in the industry.
• Work from anywhere, on your hours; coworking access when you want a change from home.
• Health cover for you and your family.
• Real international exposure: clients and partners across the Gulf, Europe, Africa, India, APAC and the US.
• A yearly offsite that we take seriously, and a culture that does not.

Datahash is an equal-opportunity employer. We hire on evidence and fit for the work and do not ask for salary history. Compensation is discussed at the fit-and-terms stage.

Originally posted on Himalayas