Head of Engineering

We're building Skyway, a platform to help companies find, procure, manage, and forecast hyperscaler, inference, and GPU infrastructure. With the explosive growth of AI, companies are signing the largest infrastructure agreements in history and doing it all with spreadsheets and fingers-in-the-wind. Skyway provides structure and data to make the problem suck a whole lot less.

Our customers are the strategic finance and engineering leaders at AI labs, inference providers, neoclouds, and AI-native software companies who are making seven, eight, nine, ten-figure compute decisions using data that flows through our systems.

Under the hood, we're turning unstructured agreements-hyperscaler private pricing agreements, AI lab MSAs and prepaid credits, neocloud reserved capacity-into structured data, dynamically recalculating bills against both contract and public pricing, and giving finance and engineering a single shared set of numbers for what they've consumed, are consuming, and will consume.

Our goal is to be the system every AI-native company runs its compute strategy on: forecasting, scenario modeling, commitment tracking, invoice validation, benchmarking, and purchasing, all on top of the best proprietary pricing dataset in the world.

Enterprise cloud cost management is a deceptively deep problem we believe the entire cost management space is solving the wrong part of the problem.

We're looking for a Head of Engineering to own how Skyway gets built: the architecture, the velocity, the team, and the technical decisions that will compound for years. Today, this is a builder role: you'll be in the code every day and using that credibility to set the pace for everyone around you.

Why yet another cost management product?

Historically, products in this space have made three fundamental choices:

1. They provided a cost-first data model to customers
2. They assumed the provider's billing data is correct
3. They chose to solve a specific piece of the cost management problem domain

We're taking the opposite of all three: our world is consumption-first, we're recalculating the billing for each provider, and we're working to solve the entire domain.

We've started with a AWS contract management product and expanding over time to handle forecasting, scenario modeling, cost allocation, chargeback/reinvoicing, practice management, and much more. Our goal is to be the system every enterprise runs their cost management practice on.

To accomplish this, we're processing hundreds of millions of rows of billing data per customer, per month. We're turning unstructured commercial contracts into structured data and dynamically recalculating bills using rates from both the contract and public pricing data. We're building a semantic layer that translates technical cloud usage data into business concepts anyone can understand, and we're creating the world's first standardized structure for describing a commercial contract and all of its facets.

What makes this hard

There are three core issues we have to solve:

1. Near-unbounded data cardinality coupled with very large and complex datasets
2. Consumption-first means we need to model the pricing schemes and commercial contract structures for every vendor we support-and we intend to support hundreds-to-thousands
3. Hiding all of this complexity from the customer with excellent product design

Here's a sample of the kind of problems you'll help us solve:

• A customer's organizational restructure moves $40 million in infrastructure spend from one VP to another overnight-both the historical view and the future view need to be correct, but they need to be correct in different ways.
• A customer has 6,000 people in engineering, 2,000+ application IDs, and a highly matrixed reporting structure. "Who owns this?" is almost impossible to answer, but we need to make it simple. To make matters worse, engineering and finance have wildly different perspectives on this.
• A SaaS provider changes their pricing model, breaking previous known structures. We need to detect it, adapt to it, and recalculate downstream.
• Each provider has dozens of columns in its billing data. When combined with customer-provided data, the dimensions exploded to hundreds or thousands. Given the dimensionality and customer size, the dataset reaches into the terabytes easily. The customer needs to not just be able to query the data, but to also modify the data-and do so faster than a traditional data processing pipeline will allow.

What you'll do

Ship daily and set the pace. This is the single most important part of the job. You'll personally ship working capabilities every day and drive the team to do the same. You identify and remove anything that slows shipping down, whether they be tooling gaps, process bloat, unclear requirements, architectural bottlenecks. The team should be capable of shipping more in month 12 than month 6.

Build the engineering team. You're setting our engineering talent bar. Beyond hiring, you're also responsible for growing these engineers: raising their technical bar and building a culture where your team is doing the best work of their careers so far.

Make architecture decisions that last. Build on a stack and architecture that supports 10-50x growth in data and customers without a fundamental rearchitecture. Make deliberate, documented decisions about what's built to last vs. what's intentional tech debt. Architecture decisions should enable velocity, not slow it down.

Bring product ideas to the table. While your primary job is how, not what, you should have strong opinions about product improvements based on what you see in the code, the data, and customer behavior. Push back on product direction when you see technical or customer reasons to.

Be a credible technical voice externally. Participate in enterprise sales calls, customer onboarding, public promotional events, and investor meetings.

How we work

We work together in the office in San Francisco three days per week, so you must be located in the SF Bay Area and willing to work in the office on a regular basis.

You'll be given more autonomy than you're comfortable with. We're a small team, around a dozen people today. People joining now are making foundational decisions that need to hold up to future growth in data and customers. We've got a lot to do and we hire for expertise and great judgement. We value quick decision-making and a "let's find out!" attitude.

We ship daily. New engineers ship in their first week. We value making progress toward customer desires every day.

AI makes us all more capable. We use AI assistance throughout the company heavily and expect you to as well-not just tab-completion, but agents, code review, and whatever else helps you move faster. AI skeptics won't do well here.

About you

You're energized by ambiguity, comfortable tearing down what you built last month if the business requires it, and you'd rather ship something imperfect today than plan something perfect next quarter.

Data infrastructure experience is particularly valuable-large-scale ETL/ELT, columnar storage, etc. Our stack today is Python, Flask, ClickHouse, Airflow, React, and Typescript. The stack will evolve, though, and what matters more than any specific tool is that you have informed opinions about data systems and the experience to back them up.

You're someone who's built and recruited engineering teams before. You might be a Director of Eng or VP of Eng. You might have built your own startup in the past and want to do it again, or maybe you're thinking about that move for the first time.

You don't need to have domain expertise in FinOps or cloud billing. We'd actually prefer you don't come from another FinOps vendor-we're doing something new and don't want inherited assumptions about what the product should look like. What you do need is genuine curiosity about hard data problems.

This role is not for you if...

• You're looking for a role where you manage people but don't write code. This is a builder-first role.
• You expect well-defined scope and established processes. We're a seed-stage company and things change constantly.
• Your background is primarily SRE or infrastructure operations. We need a product engineering leader.
• You're coming from a traditional FAANG or traditional enterprise environment.

Our unfair advantages

We've been doing customer research for seven years. Duckbill started life as services firm in 2019 and has worked primarily with large enterprises. Through that work, we've had a front row seat to the hardest problems organizations face. As a result, our vision for Skyway isn't built on hypotheses: it's built on having directly worked these problems alongside the people who live with them every day.

Services are a superpower. Every services engagement teaches us something new about customer pains and desires, creating an intelligence loop between services and product. At the scale we're working at, we are exposed to the hardest cloud cost management problems in the world and can turn those insights directly into product capabilities. (and don't worry, our engineering team is separate from our services team, so no FDE here)

Everyone knows us. We operate the largest AWS community outside of AWS itself-35,000 newsletter subscribers, 5 million podcast downloads, 100,000+ social media followers. Nearly every Fortune 100 company on AWS knows who we are and will take our call. We aren't having to build a sales pipeline from scratch.

Compensation & Benefits

Compensation for this role is a salary range of $200,000-$250,000 plus substantial early stage equity options. We provide a 401(k) and healthcare, vision, and dental with premiums fully covered by the company. Dependents are covered 50%. We offer 4 weeks of PTO, plus unlimited sick leave.

Our unfair advantages

We've been doing customer research for seven years. Duckbill started life as small boutique services firm in 2019 working primarily with large enterprises. Through that work, we've had a front row seat to the hardest problems organizations face. As a result, our vision for Skyway isn't built on hypotheses: it's built on having directly worked these problems alongside the people who live with them every day.

Services are our superpower. Customers seek us out for our contract negotiation expertise, process, and proprietary benchmark data, a motion that started with AWS and expanded to Azure, GCP, and now to inference and neocloud providers. Every negotiation engagement teaches us something new about customer pains which feeds right back into the product, creating an great feedback loop between services and product. (And don't worry, our engineering team is separate from our services team, so no FDE here.)

Everyone knows us. We operate the largest AWS community outside of AWS itself-35,000 newsletter subscribers, 5 million podcast downloads, 100,000+ social media followers. That reach, plus the relationships we have inside the hyperscalers, AI labs, and neoclouds, means we aren't building a sales pipeline from scratch.

About Duckbill

We are a small and growing team of ~15, which means you get the opportunity to be on the ground floor of building the product and company. Our founders are domain experts in the market+problem space, bringing deep industry and customer connections in cloud and compute economics to the product.

We're backed by Heavybit and Uncork Capital, having raised $7.75 million, along with substantial revenue.