I’m Christopher “Skippy” Pate — an engineering leader who has scaled teams, rebuilt brittle systems, led company-wide operating changes, shipped through high-pressure moments, and stayed technical enough to understand the hard parts firsthand.
“What would Chris do?” is a question I ask myself.
He set a high standard for working with Product and Design and was directly responsible for the success and growth of several engineers around him.
“One of the highest-performing teams I’ve ever worked with.”
Strong technical chops, superb communication, thoughtful use of analytics, and a consistent focus on user and business impact.
“Everyone loved/loves Skippy.”
A former Attentive colleague used that line while introducing me to another technical leader after I had already moved on.
“Current badass engineer.”
That was how an ex-Attentive colleague introduced me to Aisle’s CEO when connecting us for the VP Engineering role.
“He was dealt a 2–3 offsuit hand — and won.”
A line from Eric Miao about one of the hardest cross-company pushes I led at Attentive — the kind of work that helped define my progression into Director-level leadership.
“He didn’t build a company. He raised one.”
“He taught. He listened. He waited. And somewhere along the way — we grew up.”
My best work has come from understanding the “why,” choosing the right problems to own, and building systems — technical and organizational — that make the people around me more capable.
Requirements are the beginning of the conversation. I want engineers to understand the business outcome, the constraints, and where the system may need to evolve.
Some of the highest-impact work in my career was never assigned to me. Ownership means seeing the important problem, doing the homework, and bringing others with you.
Good operating systems create visibility and predictability without forcing teams to spend their lives feeding the process.
The goal of leadership is not to become indispensable. It’s to leave behind stronger people, better systems, and an organization that can operate without you.
This is not a title list. Each stop changed how I think about engineering leadership.
What I walked into: a team talking about a June 29 MVP date without a shared definition of what the MVP actually was. Product, engineering, and business expectations were not aligned.
First two weeks: I earned enough trust from the departing CTO to take over execution, clarified the MVP, reset priorities, and made the work legible to both engineers and business stakeholders.
Technical reset: we reworked the claims flow, made major data-model changes down through procedures, and rebuilt parts of the platform that were not suitable for the product we were trying to launch.
Operating model: introduced clearer sprint goals, retrospectives, on-call expectations, interview structure, performance reviews, and quarterly-goal participation without turning the team into a process factory.
Broader ownership: infrastructure, Datadog ownership, HIPAA compliance, Vanta, AI enablement, and hands-on coding all sit inside my scope. I am also running an AI-enablement session for the broader company around instruction files and practical adoption.
People signal: I have heard directly from engineers that my presence is a meaningful reason they stayed through the transition. That is not a resume bullet; it is one of the clearest signs that the team reset was working.
Operating reset: when I joined, requests came through random DMs and Slack threads, priorities shifted constantly, and delivery was highly reactive. I introduced Jira plus a cross-functional intake board so Product, CX, Sales, and the CEO could surface work without routing everything through individual engineers.
Hiring and growth: scaled the engineering team from roughly 4 to 9, introduced an engineering ladder, and built an interview process the company trusted enough to reuse when hiring a Head of Product. The process produced strong close and retention rates, including two tech-lead hires I consider especially successful.
Billing: owned requirements, architecture, Stripe setup, and implementation of a new billing platform while leading a six-person team. The system supported flat fees, percentage/bucketed pricing, upfront brand funding for shopper reimbursement, partner modifiers, and changing billing triggers. What used to require duct tape or rebuilds became week-scale iteration.
Fraud: built a multi-signal detection approach using duplicate receipt reuse, receipt-edit signals, known store/transaction patterns, device-like fingerprints, and model-based classification. Suspected fraud fell from roughly 5% to roughly 0.5%, after which it stopped being a recurring business concern.
Store locator: built the backend for a product that brands could embed in their Shopify or web properties. It serves around 200K requests/month. The supporting scraper platform processes about 11M records nightly across roughly 200 brands in under three hours.
AI-assisted engineering: after manually reverse-engineering the first scrapers, I helped define a standard scraper interface/base class and used AI coding tools to automate much of the reverse engineering. Typical scraper implementation dropped from about 1–2 weeks to 1–2 production days.
Evidence from the team: the farewell site the team built for me framed my role as teaching, listening, and stepping back so they could operate independently. That is the leadership outcome I care about most.
Career arc: joined as a senior engineer, became a manager, then Director. At peak scope I had 3 managers and 28 total engineers in my org.
Fashion Nova / multi-number: helped redesign a deeply embedded one-company/one-phone-number assumption so Attentive could support multiple numbers end to end. The first major launch supported a customer several times larger than any prior account and went live the Sunday before BFCM without a hitch.
War-room fix: I was not on the team owning a production query problem, but researched it independently, split a costly OR into a UNION, validated the plan with timings and EXPLAIN output, and delivered roughly a 20x performance improvement.
Identity and reporting: built the first versions of both systems. The identity architecture later supported billions of records.
Instagram swipe-up: backed another engineer’s idea because the effort/ROI looked unusually strong. The first launch added 500K+ subscribers in roughly an hour, triggering a huge burst of downstream messages while the signup architecture held.
Creative fatigue: challenged an assumption that a server-side “render” meant a user had actually seen a creative. Moving the fatigue signal to the browser produced a roughly 20% conversion lift; for this portfolio I am conservatively using ~20K additional subscribers/day rather than the larger back-of-the-envelope estimate from memory.
Compliance sprint: coordinated roughly 180 people across Engineering, Design, CX, TAM, and other groups to move SMS legal/compliance coverage from effectively zero to 99.8% in about 2.5 weeks across ~1,000 clients and ~1,200 legacy creatives.
Creative Builder: pushed early for self-service; later led a 16–17 person, roughly three-month effort when it became an urgent company priority.
Jira operating model: created a Theme → Roadmap Item → Epic hierarchy with automations and executive reporting so Product, Design, and Engineering could share portfolio visibility without forcing teams into high-maintenance reporting rituals.
Internationalization: led a six-to-seven-month cross-team program spanning region-specific creatives, languages, geographies, and shared data-model changes. I drafted the initial system design and reset unrealistic timing expectations with leadership.
Leadership lesson: after receiving an underperforms review despite quietly preventing fires, I learned that invisible impact can be mistaken for absent impact. I changed my leadership operating rhythm to make risk reduction and team support visible without turning work into self-promotion theater.
Rapid progression: hired as a mid-level engineer and double-promoted to Tech Lead within roughly five to six months.
Mobile ownership: took over the mobile API and Android app, led Android hiring, onboarded another engineer into API ownership, and worked closely with iOS counterparts.
Paid subscriptions: led subscription onboarding with a senior frontend engineer, which pulled me into more product and business accountability.
BIRTA: helped lead a major modernization program to break apart the monolith, upgrade Symfony across several major versions, improve page-render performance, and create reusable white-label patterns. The work involved roughly a third of engineering resources across frontend, backend, QA, and DevOps.
Object Translation Bundle: created a config-driven object/data mapping and adapter layer with Redis caching and composition patterns for third-party and internal data. It became a major reusable component of the modernization work.
Leadership evolution: this was where I learned to lead while leaning heavily on specialists around me. My job was increasingly to absorb, connect, coordinate, and translate between technical and business concerns rather than be the best person in every technical domain.
First engineer: joined what I describe as a legal-Uber-before-Uber black-car startup and built the earliest operational systems with a CEO and a more infrastructure-focused CTO.
Pricing engine: implemented complex pricing based on client, vehicle type, service type, street-defined NYC zones, boroughs, towns, counties, tolls, and late fees, backed by 70+ behavior-driven tests.
“Draw the zones on a map”: the CEO casually described the capability; I built it overnight.
OCR receipts: built receipt scanning/matching to connect manual paper workflows back to digital jobs.
Double overnighter: when a major global limo customer needed pricing functionality that did not exist, I built importers, calculators, landmark support, map timing, and operational tools from about 3 PM Monday to 10 AM Wednesday so the business could make the meeting.
What changed for me: GTN is where I internalized that engineering quality is subordinate to business viability. Great technology attached to a failing business still fails.
Short enough to scan. Deep enough to understand how I make decisions.
A two-day bet on Instagram swipe-up turned into one of the company’s biggest growth wins.
I backed another engineer’s idea hard enough to put my reputation behind it. The first launch drove 500K+ subscribers in about an hour. The signup path — the part I was most worried about — held under a load it had never seen before.
Moving fatigue accounting from “server said render” to “browser actually rendered” changed the business curve.
At the scale involved, that translated to roughly 20K additional subscribers per day. It was a two-week project that proved a small architectural assumption could hide a very large business opportunity.
~180 people, ~2.5 weeks, one hard external deadline.
I coordinated Engineering, Design, CX, TAM, and others across dynamic and legacy creative systems, welcome messages, Terms/Privacy pages, and SMS-length constraints. The point was not technical heroics; it was creating enough clarity that two-thirds of the company could move together.
Built the backend and helped lead a ~2.5–3 month company push.
The platform handles ~200K requests/month. Scraping architecture processes ~11M records nightly across ~200 brands in under three hours. AI-assisted reverse engineering helped compress scraper implementation from roughly 1–2 weeks to 1–2 days.
A multi-signal fraud model made a material business problem small enough to stop being a recurring fire.
Duplicate receipt reuse across numbers, receipt tampering patterns, store/transaction characteristics, device-ish fingerprints, and learned/model-based signals were combined to reject suspicious submissions.
Replaced a brittle subscription-only model with a durable pricing platform.
The new system supported flat fees, percentage/bucketed economics, upfront brand funding for shopper reimbursement, partner modifiers, and evolving billing triggers. Pricing changes that used to imply duct-tape or rebuilds became week-scale iterations.
These are the details I would normally only get to explain in an interview. Open any one.
A performance win that immediately taught me the difference between fast code and production scale.
At Mortgage Success Source, a conference roadmap PDF took double-digit seconds because of repeated simple queries inside loops and old PDF-generation cruft. I profiled it and got generation below one second. Then roughly 2,000 attendees hit it at once and our on-prem servers still folded. It was an early lesson that local performance and system capacity are different problems.
Nuance taught me what kind of environment I did — and did not — want to lead.
I was staffed across several IVR consulting teams, finished assigned work quickly, and used spare time to learn. I was criticized for logging roughly 45 hours while peers logged closer to 55. The lesson I carried forward was simple: incentives shape behavior, and healthy organizations should value useful outcomes over performative activity.
Intermodal was where initiative stopped being enough.
I spent much of my time improving duplicated pages/APIs and prototyping a buffered ExtJS data grid because the existing interface effectively loaded everything. A lead pushed back while another senior engineer supported the work. The lesson was not “stop improving things”; it was that good ideas without alignment can still fail organizationally.
The GTN double-overnighter.
A customer needed town/county/state pricing, stops, landmarks, imports, map timing, and a usable calculator. I started Monday afternoon and finished Wednesday morning. The story matters less because of the hours than because it changed how I thought about technology: sometimes the correct engineering decision is the one that gives the business a chance to survive.
The pattern behind many of my highest-impact projects.
From BI onward, I increasingly stopped treating requirements as instructions. I want to understand why the request exists, what business outcome matters, who needs to believe in the solution, and how likely the problem is to recur. That thinking led to reusable infrastructure at BI, major growth bets at Attentive, and more durable billing/operating systems at Aisle.
A painful performance review changed how I communicate.
At Attentive, I was managing managers and an increasingly large scope when I received an “underperforms” review. My reaction was essentially, “You have no idea what I do all day, do you?” My manager saw fewer escalations than peers; I saw that as evidence that problems were being resolved before they reached him. I learned that leaders must make hidden risk reduction visible, not assume the organization will infer it.
The Jira pattern I reused later at Aisle.
At Attentive, executives needed portfolio visibility as the company grew. I built a Themes → Roadmap Items → Epics model with automation around milestones so leadership got reporting without forcing teams to manually maintain a parallel project-management universe. At Aisle, I applied the same philosophy to intake: centralize visibility, not decision-making overhead.
The idea behind the Aisle farewell page.
The most meaningful description of my leadership did not come from a performance review. It came from the team: “He taught. He listened. He waited. And somewhere along the way — we grew up.” That is much closer to how I measure leadership than headcount alone.
How I think about AI inside engineering organizations.
I use AI to compress implementation time, structure repeatable workflows, improve documentation and instruction quality, and raise the floor for the broader team. At Aisle, AI-assisted reverse engineering changed scraper delivery from week-scale to day-scale. At Torch, I am helping make AI usage more repeatable across the organization through instruction files, ticket-to-draft-PR workflows, and internal enablement.
Plenty of engineering leaders can say they use AI at work. What differentiates me is that I keep using it after work: to build, automate, learn, experiment, and turn ideas into working systems. That matters because my point of view on AI is not coming from a mandate or a quarterly initiative. It comes from repeated hands-on use across very different problem spaces.
My interest is less “Which chatbot did you adopt?” and more: how does AI change the shape of engineering work? What context should live outside the model? What should a human own? What can an agent safely execute? How do voice, persistent knowledge, coding agents, and repeatable instructions fit together? I explore those questions both inside companies and in my own projects.
A GitHub-backed personal knowledge system designed to make AI context durable, inspectable, and reusable across tools.
I wanted conversations, project context, decisions, templates, and instructions to stop disappearing into isolated chat histories. The project explores a deeper question: what should persistent context look like when multiple AI systems and coding tools are working with the same person?
An ongoing experiment in closing the gap between talking through a feature and having an agent begin implementation.
The goal is not fully autonomous software development. It is controlled handoff: discuss requirements conversationally, make the intent explicit, preserve context, then let a coding agent pick up implementation while I retain control over major architectural or product decisions.
I spend real time on the quality of instructions, examples, workflows, and context that shape AI output.
AI capability is increasingly constrained by how well the surrounding system communicates intent. I experiment with “instructions for instructions,” examples, iterative refinement, and reusable workflows because good AI adoption is an engineering-design problem, not just a prompt-writing problem.
I use AI to accelerate actual products and experiments rather than only internal productivity tasks.
Projects have included a preventive-property-maintenance app, workout tooling, stock/trading scanners and scripts, a recipe manager concept, a kids learning app, this digital resume, and other small product experiments. The common pattern is rapid exploration followed by working software.
I’m interested in AI when it intersects with physical systems, routines, and messy real-world constraints.
My personal projects span home automation, energy experiments, financial workflows, and family systems. That breadth forces me to think about AI as an orchestration layer around imperfect data and human judgment — not as a clean demo running in isolation.
The personal experimentation feeds directly back into how I help engineering organizations adopt AI.
At Aisle, AI-assisted reverse engineering helped compress scraper implementation from roughly 1–2 weeks to 1–2 days. At Torch, I’m helping standardize AI usage through instruction files, ticket-to-draft-PR workflows, and broader engineering enablement. The difference is that I’m bringing an already-developed point of view into the workplace — not discovering AI because the company asked me to.
When I left Aisle, the team created a full interactive farewell site around a “gentle parent” metaphor. It is funny, personal, and unusually revealing: the team’s story was not that I carried them — it was that I taught, listened, stepped back, and left them more capable.
I’m most useful where there is complexity to simplify, a team to grow, and real business outcomes attached to the work.