Staff Software Engineer (Hands-on Architect)

About this role

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

We are looking for an experienced, hands-on engineer who can take a product from an initial idea to a reliable production system, owning technical discovery, architecture, implementation, deployment, and continuous improvement.

Think of this as operating with the independence and breadth of a small product engineering team. You should be comfortable turning ambiguous requirements into working software, making pragmatic architectural decisions, and moving across the stack to deliver complete product capabilities.

This is a senior individual contributor role with substantial technical ownership. You will write code, solve difficult engineering problems, and remain accountable for what you ship. Strong use of AI coding tools and agents is central to the role, backed by the engineering judgment to validate their output.

What you will own

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End-to-end product development. Translate business problems into technical plans, working prototypes, production features, and subsequent improvements.

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Architecture and technical decisions. Design systems that balance speed, simplicity, security, reliability, scalability, and cost.

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Full-stack implementation. Build user interfaces, backend services, APIs, integrations, data models, and background processing workflows.

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Production delivery. Own testing, CI/CD, cloud deployment, observability, troubleshooting, and performance optimization.

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Technology evaluation. Quickly learn unfamiliar technologies, test assumptions through focused experiments, and choose tools based on the problem.

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AI-assisted engineering. Use coding assistants, agents, and automation to accelerate research, implementation, testing, debugging, and documentation.

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Quality and maintainability. Review and validate all code, including AI-generated code, and establish appropriate tests, documentation, and operational safeguards.

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Product collaboration. Work directly with product and business stakeholders to clarify requirements, challenge assumptions, define scope, and deliver useful increments.

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Technical influence. Raise engineering standards through design discussions, code reviews, reusable components, and knowledge sharing.

What success looks like

You can take a loosely defined product need, propose a sensible approach, and deliver a complete, usable solution with minimal coordination overhead. You use AI effectively to shorten delivery cycles, make sound technical decisions, and leave behind software that is secure, observable, maintainable, and ready to evolve.

How we hire

We move fast and we are transparent about how we decide. 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 exercise close to the real work, which you present to two or three of us. Step 4: fit and terms, including the founder for this role. 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.

What to share when applying

Share one or two products or substantial features you personally took from concept to production. Explain what you owned, the key architectural trade-offs, how you used AI or automation, and the outcomes achieved. Links to working products, code samples, or architecture walkthroughs are welcome.

Requirements

What we are looking for

• A strong track record of independently building and shipping production software with ownership across multiple stages of the product lifecycle.

• Deep expertise in at least one backend ecosystem and strong working capability across modern frontend development, databases, APIs, and cloud infrastructure.

• Broad understanding of tools, frameworks, and architectural approaches, with the ability to explain their trade-offs and select the simplest effective solution.

• Demonstrated ability to become productive quickly in unfamiliar codebases and technologies.

• Sound understanding of system design, data modeling, asynchronous processing, caching, queues, authentication, authorization, and application security.

• Hands-on experience with automated testing, CI/CD, containers, monitoring, and production incident diagnosis.

• Practical experience using AI development tools for meaningful engineering work, including breaking work into tasks, providing context, reviewing changes, and verifying results.

• Strong debugging skills and the ability to reason across application code, infrastructure, data, and third-party integrations.

• Product judgment: the ability to identify what matters, reduce unnecessary scope, and make sensible build-versus-buy decisions.

• Clear communication, strong ownership, and the ability to make progress with limited supervision.

How you approach the work

• You turn an unclear problem into a concrete plan and start delivering.

• You know when a prototype is sufficient and what must change before it reaches production.

• You use AI to increase your capacity while retaining a clear understanding of the systems you build.

• You can distinguish a useful abstraction from…