You need a right hand who's technically strong and commercially sharp — someone who can go deep with data scientists and engineers, and still be the trusted voice in a C-suite room. That's the seat I'm built for.
I don't write code — I support the engineering teams that do, and I architect the process and priorities around them. When I took over an engineering team that hadn't shipped in six months — busy, but with pull requests stacking up as a stand-in for progress — I reset the scrum cadence, rewrote the roadmap, and fixed the operating model that was blocking the talent. The relaunched product scaled past 11,000 accounts in 18 months and lifted revenue 20%.
I drove the functional spec for our Model Context Protocol server, shipping this year — owning what we build and why, advising the executive team on data protection and identity, and making the access and risk calls before the architecture decisions. I stand up the operating backbone too: intake, prioritization, delivery cadence, measurement. The layer where AI value is actually realized.
I supported the acquisition of our technology and the asset-contribution agreement that followed. I met weekly with our investors to report momentum and co-developed the pitches behind the deal. After it closed, I supported the inter-company transfer-pricing agreement and the implementation strategy as the technology moved toward an IPO. Today I own C-suite relationships on accounts worth $1M+ a year and run a national line of business.
My M.S. in Information Technology with a major in Cybersecurity from Lawrence Technological University was built for exactly this moment — the years when enterprises move from AI experiments to AI in production, and the governance question stops being theoretical.
On our MCP server, I set the access boundaries and made the identity decisions before the architecture was locked — because retrofitting governance onto a shipped system is how enterprises end up exposed. Data lineage and identity come first, model selection second. It's the only order that scales.
When building for security meant slowing monetization, I chose security — and then defended that call weekly to investors who were waiting on a return. Responsible AI isn't a framework you cite in a deck; it's the decision you make when the safe choice costs you something. I've made it, under real pressure, more than once.
I built the prioritization framework that weighs security, automation, and monetization on every release — so governance is a checkpoint in how we ship, not a fire drill after something breaks. Intake, prioritization, delivery cadence, measurement: the scaffolding that decides whether AI investment compounds or evaporates.
You're growing the AI & Advanced Analytics practice in Western Canada. Two clocks running: delivery quality across concurrent engagements and pipeline that compounds. My job is to make sure neither slips while we scale.
This is a starting hypothesis, not a finished plan. Show me the good, the bad, and the ugly of where the practice is today, and the plan gets sharper.
This chatbot is trained on my background — technical depth, commercial track record, leadership style, and how I'd think about Western Canada AI. Ask it anything you'd ask me in an interview.
Hi Danielle — ask me anything about Sierra's experience, leadership style, or why she's drawn to this role at PwC.
One call. If by the end you don't see the fit, we both walk away with our time well spent. If you do — we get to work on Western Canada.
Book a 30-min call →