Forward Deployed Product Manager, Internal Tools & Automation

Giga is hiring a Forward Deployed Product Manager, Internal Tools & Automation to build the internal products that keep a fast-growing physical operation moving. You will learn how work actually happens, define the records and ownership that make it reliable, and work hands-on with Giga’s software builders to turn fragmented handoffs and repetitive work into focused systems, integrations, automations, and AI-assisted workflows.

An early focus will be the path from customer specifications and orders through sourcing, purchasing, cost tracking, invoicing, and fulfillment. You will make the workflow, data, permissions, and decision rules reliable, then automate the parts where software or AI can improve speed, accuracy, or visibility. A company-wide enterprise resource planning (ERP) replacement is outside the scope. Success means trustworthy records, faster decisions, fewer manual reconciliations, and changes that operators adopt.

This is a forward-deployed internal-tooling product role within Digital Operations, focused on Business Systems & Automation. You will embed with operators, work close to the data, and partner with Giga’s software builders from discovery through supported production and adoption. This is a hands-on individual-contributor role: you will focus engineering time on the highest-value problems and stay with each change through adoption and measurable results. The answer may be a better process, an integration, deterministic automation, an AI-assisted workflow, a vendor tool, new software, or ending work that no longer makes sense.

This is a full-time, 100% remote role with no recurring office requirement. We have a slight preference for candidates based in or near Austin, Texas, but Austin is not a requirement. You may work from any location where Giga is able to employ you. Because the work spans operators, functional leaders, and software builders, you will need strong written communication, reliable availability during agreed overlap hours, and consistent follow-through.

Occasional travel may be required for company onsites, workflow discovery, or major rollouts.

• Discover and document critical workflows by working directly with the people who operate them.
• Define the core records, identifiers, lifecycle states, owners, decision rules, and exceptions that make each workflow reliable.
• Use SQL and direct data inspection to reconcile conflicting records, test assumptions, and identify data-quality problems.
• Map how information moves through business systems, spreadsheets, documents, application programming interfaces (APIs), and manual handoffs.
• Identify workflows and decision points where deterministic automation, AI assistance, or both can improve speed, accuracy, or visibility. Prototype with clear permissions, human review, and failure paths, then expand what proves useful.
• Sequence process changes, integrations, workflow automation, and internal tools into the smallest releases that deliver a useful outcome.
• Write clear workflow briefs, requirements, data contracts, acceptance criteria, and decision records.
• Partner with engineers on architecture and delivery while owning the business outcome, roadmap, and adoption.
• Lead rollout, training, migration, and support planning, then retire the workarounds each release replaces.
• Measure each release by its effect on cycle time, accuracy, visibility, control, and manual effort.
• Work with the appropriate owners to establish practical permissions, auditability, and data-handling controls.
• Build and maintain a focused roadmap based on business value, risk, dependencies, readiness to adopt, and available engineering capacity.
• Keep distributed business owners and software builders aligned through concise writing, clear decisions, and focused working sessions.

• Demonstrated experience owning a cross-functional workflow or internal product from an ambiguous starting point through delivery, adoption, and measurable operating results.
• Strong process-discovery skills, including the ability to uncover undocumented rules, exceptions, ownership gaps, and conflicting definitions.
• Practical fluency with relational data, including entities, identifiers, joins, grain, one-to-many relationships, lifecycle states, lineage, and data quality.
• Ability to write and review SQL to inspect, join, reconcile, and validate operational data.
• Working knowledge of APIs, integrations, authentication, events or webhooks, failure handling, and system boundaries.
• Strong product judgment, including the ability to scope a useful release, choose what to build next, and explain what should wait or be declined.
• Experience working directly with software engineers while retaining ownership of the business outcome and user adoption.
• A record of influencing technical and operating teams whose priorities and systems you do not directly control.
• Ability to build trust and communicate clearly with frontline operators, functional leaders, and technical teams in a fully remote environment, including when processes and data are incomplete or undocumented.
• Hands-on experience designing, prototyping, or shipping workflow automation, including AI-assisted tools, with sound judgment about when to use deterministic logic, AI assistance, and human review.

• Experience in manufacturing, supply chain, procurement, logistics, construction, project delivery, or another physical operating environment.
• Experience connecting commercial, operational, procurement, and financial records.
• Familiarity with NetSuite, HubSpot, order-management tools, procurement platforms, or document-based request-for-quote (RFQ) workflows.
• Experience building internal products, data products, enterprise applications, or operational platforms.
• Experience improving data quality in live operations and retiring spreadsheets or local tools after a replacement proves reliable.
• Familiarity with role-based access control, auditability, sensitive data, and secure read/write interfaces.
• Demonstrated ability to reason about complex systems, including dependencies, bottlenecks, feedback loops, and failure modes. Evidence may come from work, open source, education, simulations, or systems-oriented hobbies.
• Hands-on experimentation with AI agents and local, hosted, or hybrid model architectures, with a reasoned point of view on permissions, reliability, observability, cost, and maintenance. Professional, open-source, academic, and personal projects all count.