Archived listing. The details below describe a past opening.
Stacktics is a growing marketing analytics consultancy, 20 to 100 people and expanding that works with enterprise brands in CPG, retail, telco, and financial services. Our clients spend real money on media and need to know what's working. We measure it, model it, and build the infrastructure that makes measurement repeatable.
Our practice runs on data science, data engineering, and cloud infrastructure all on GCP, all client-facing, all carrying real commercial stakes. Engineering here is not a cost center that delivery hands work to. It is a strategic function that shapes what we sell, how we scope it, and whether we can defend the number in the room.
THE ROLE
This is the senior-most engineering role at Stacktics, and it is deliberately not a pure-strategy seat. You set the multi-quarter technical direction for the practice, and you stay hands-on across four specific commercial and technical motions that most "Head of Data Systems" titles elsewhere delegate away: writing and negotiating Statements of Work and SLAs, owning solution architecture across engagements, standing in the room for client pitches and RFP responses, and authoring the technical narrative behind our SR&ED claims.
AI is compressing the execution layer of engineering work across the practice. That changes what you're hired to protect: not typing speed, but the judgment that turns a vague client ask into an accurately scoped SOW, a sound architecture, a pitch that survives technical scrutiny, and an SR&ED claim that reflects real technological uncertainty rather than routine engineering.
KEY RESPONSIBILITY:
Statements of Work & SLAs
Author and negotiate SOWs directly with enterprise clients and their procurement/legal teams scope, deliverables, definition-of-done, and the SLAs that back them. You write the language that Stacktics is held to.
Client Pitching & Business Development
Represent Stacktics technically in new-business pitches and RFP responses. Translate what's technically possible into a proposal a client-side buying committee will trust, and defend it live when challenged.
Solution Architecture
Set and personally review the GCP architecture behind every major engagement data models, pipeline patterns, MLOps conventions, AI-tooling standards. Your sign-off is the gate before an architecture goes into a SOW or a client-facing pitch.
SR&ED Documentation
Own the technical narrative for Stacktics' SR&ED claims, identifying and articulating the technological uncertainty and systematic investigation in our engineering work in terms that satisfy CRA requirements, working alongside our SR&ED preparer/consultant.
Engineering Strategy & Roadmap
Set the multi-quarter technical direction for the practice: what we build once and productize, what stays bespoke, where the team invests in capability ahead of demand.
Team & Practice Leadership
Own hiring, career development, and technical standards across the engineering org, including the Senior Technical Lead and reporting Data Engineers/Scientists. Mentor the people who will eventually take pieces of this role off your plate.
AI Development Lifecycle (ADLC)
Continue evolving Stacktics' ADLC, our AI-native answer to SDLC. Pilot new stages against real engagements, refine the framework as AI tooling shifts, and drive adoption across the engineering org.
Engineering Best Practices & Standards
Own and continuously improve the practice's engineering standards the AI coding constitution (CLAUDE.md, GEMINI.md, .cursorrules), PR checklists, and slash commands keeping them current as tools, clients, and the team's needs evolve.
LEADING IN THE SOFTWARE 3.0 ERA:
AI tools have changed how fast engineering work gets produced. Your job is to make sure that speed shows up as better-scoped SOWs, sounder architecture, sharper pitches, and stronger SR&ED claims not just faster shipping of the same judgment gaps.
Scope and price for the AI era
Estimation habits built for pre-AI delivery timelines will misprice engagements in both directions. You recalibrate how the practice scopes and prices work as AI tooling changes what a team can actually deliver in a sprint and you write that recalibration into the SOWs you own.
Architect for AI-accelerated delivery
Set architecture and coding standards that assume engineers are using AI assistants daily code review habits, eval frameworks, and quality gates sized to the failure modes of AI-assisted development, not just its speed.
Defend the work in the room
A pitch deck full of AI-generated confidence is a liability, not an asset. You're the person a client's technical stakeholders can push back on and get a real answer from about the architecture, the timeline, and why the number in the SOW is the number.
Prove the R&D, don't just claim it
AI-assisted delivery raises a real question for SR&ED eligibility: routine application of known tools isn't a claimable technological uncertainty. Part of this role is knowing that line and building the documentation habits across engineering that capture genuine R&D as it happens, not reconstructed after the fact.
QUALIFICATIONS:
- 12+ years in engineering, including 5+ years in a senior technical leadership role inside a consultancy, agency, or professional-services environment
- Direct experience authoring and negotiating Statements of Work and SLAs with enterprise clients, you can point to SOWs you personally wrote
- Hands-on solution architecture experience at enterprise scale; GCP-native strongly preferred (BigQuery, Vertex AI, Composer, Dataflow)
- Comfortable as the technical voice in a client pitch or RFP response, able to hold up under direct questioning from a client's technical evaluators
- Exposure to or ownership of SR&ED claim documentation, or the demonstrated ability to write clearly about technological uncertainty and systematic investigation for a non-engineering audience
- Active daily user of AI coding assistants (Copilot, Cursor, Claude Code, or equivalent) and fluent in what they change about estimation, architecture, and review
- Experience building or evolving an AI-native development lifecycle and the standards that support it (coding constitutions, PR checklists, review gates) not just following one someone else wrote
Strong plus
- Prior experience in marketing analytics, MMM/attribution, or adjacent MarTech domains
- Existing working relationship with an SR&ED consultant or preparer
- Track record growing an engineering function's commercial scope not just its headcount
