Principal Technical Product Manager — AI, DSP & DSPM

DataStealth•Mississauga, ON

7d•CA$140,000 - CA$150,000•Hybrid

About The Position

The Principal Technical Product Manager is the most senior individual-contributor product role on the DataStealth Data Security Platform (DSP) and our Data Security Posture Management (DSPM) capabilities — how we discover, classify, prioritize, and protect sensitive data across cloud, hybrid, and on-premises estates, and how AI agents do that work autonomously and safely. This is a superset role: you own the full product, end to end, from market and customer strategy through technical architecture and delivery. Reporting to the Director of Product Management, you own strategy, roadmap, and execution for your domain. You own the business and customer side — the market, the buyer, positioning, pricing and packaging, and the commercial outcomes — and you own the technical side at the depth this platform demands: operating at the architecture level with engineering, writing specs precise enough to build from, and prototyping or pressure-testing the riskiest ideas before the team commits. Most product roles do one of these well; this role requires both. You partner with the Director of Product Management and the broader product team on strategy, with the CTO and engineering leadership on platform direction, and day-to-day with Engineering, Design, Sales, and Customer Success. As the principal product voice in your domain, you also raise the bar for the craft of other PMs through exemplar specs, discovery practice, and mentorship.

Requirements

10+ years of product management experience, with a track record of owning technical or platform products end to end — strategy, business case, roadmap, discovery, and delivery.
Demonstrated business and customer ownership — you own the market and the buyer, make sharp positioning and pricing/packaging calls, are credible and effective in front of enterprise customers, and tie product decisions to revenue and retention.
Deep technical fluency — you read an architecture diagram, reason about APIs, data models, and data pipelines, and make informed technical trade-offs with engineering. Expect to discuss specific products you shaped and bets you personally de-risked.
Firsthand experience shipping AI/ML or agentic features — you have shipped a product that uses ML or LLMs in production — ideally agentic systems that take action — and you have clear, current opinions on where AI in security is genuinely heading and where it is hype.
Experience building security or data products — data security, DSPM, DLP, CASB, cloud security, data governance, or an adjacent data/infrastructure domain — with an understanding of how enterprises discover, classify, and protect sensitive data.
Cloud fluency — hands-on understanding of how products run across AWS, Azure, or GCP, and ideally hybrid or on-premises environments.
Exceptional written communication — you write specs and strategy documents that are precise, decisive, and a pleasure to build from, and you communicate crisply with both engineers and executives.
Proven ability to influence senior engineers, executives, and customers through RFCs, specs, exemplar decision documents, and product vision — not positional authority.
Deliberate use of agentic AI tooling (Claude, Copilot, Cursor, and similar) as a research and prototyping force multiplier, with rigorous validation of anything that informs a decision.
Comfort working in a hybrid environment (Mississauga office, 4 days/week).
Expert in Agile/Scrum methodologies, JIRA, and experience writing epics and user stories.

Nice To Haves

Direct experience with DSP/DSPM or data security products — discovery, classification, posture, and remediation.
Experience defining agentic AI products — tool use, orchestration, guardrails, human-in-the-loop gating, and prompt-injection hardening.
Working knowledge of MITRE ATLAS, NIST AI RMF, and the OWASP LLM/ML Top 10, and how they shape AI-security requirements.
Familiarity with how DSPM integrates with SIEM, SOAR, ticketing, and cloud-native security tooling.
Hands-on or technical background — you have written code, shipped systems, or worked in engineering before moving into product.
Experience selling and shipping to large, regulated enterprises (financial services, healthcare, government).

Responsibilities

Own the strategy, roadmap, and execution for the AI-First Data Security Platform and our Data Security Posture Management capabilities — data discovery, classification, sensitivity and risk scoring, posture visibility, and automated remediation across AI, cloud, hybrid, and on-premises estates.
Make AI-driven discovery and classification the core of the product: ML- and LLM-based classifiers, embedding similarity, and context-aware sensitivity detection that find sensitive data wherever it lives — going well beyond regex and pattern matching in accuracy and coverage.
Drive autonomous and bot-driven capabilities in the solution — continuous scanning bots, auto-tagging and auto-classification, and policy-driven remediation that acts on findings with the right guardrails and human oversight.
Champion an API-native, headless platform — every capability exposed through clean, well-documented APIs so the product integrates into customers' existing applications and pipelines, and so AI agents can consume and operate it natively as first-class users.
Turn the hardest customer data-security problems into a clear, sequenced roadmap, balancing differentiation, parity, and platform health with defensible trade-offs.
Run discovery end to end: customer and design-partner interviews, win/loss and support signals, competitive teardowns, and usage data — and convert them into prioritized, well-scoped bets.
Define what success looks like for AI features: classification accuracy and coverage, false-positive cost, adoption and activation targets, and the leading indicators that show a capability is landing before revenue confirms it.
Own the commercial outcomes of your domain — understand the market, the buyer (CISO, security and data-governance leaders), and the competitive landscape well enough to make sharp positioning and investment calls.
Drive pricing and packaging in partnership with leadership, and tie roadmap decisions to revenue, retention, and expansion — not just feature delivery.
Be a credible customer-facing voice: lead discovery and roadmap conversations with enterprise customers and design partners, support key sales cycles, and represent the product externally.
Partner with Product Marketing and Sales on go-to-market — narrative, launch, enablement, and the feedback loop from the field back into the roadmap.
Write the specs, PRDs, and decision documents engineering builds on — precise on system behavior, data flows, APIs, edge cases, and failure modes, not just user stories.
Operate at the architecture level with engineering: reason about how data is discovered and classified at scale, how posture is computed, and how agents and integrations fit the platform.
De-risk ambitious work through discovery spikes, prototypes, and reference flows — especially for AI-driven classification and autonomous features — validating feasibility and value before the team commits.
Read an API contract, sketch a data model, and prototype with AI-assisted tooling well enough to pressure-test a design yourself.
Shape requirements for data discovery and posture across AWS, Azure, and GCP, alongside hybrid and on-premises estates, so coverage is broad and consistent.
Own the requirements that let one platform ship as SaaS, PaaS, and on-prem without forking the experience — packaging, configuration, and operational guarantees.
Make compliance a product capability: support PCI-DSS, SOC 2, GDPR, and related frameworks as outcomes customers can demonstrate, partnering with Security and customer teams on the evidence they need.
Shape integrations and the API surface so DSPM signal flows cleanly into the tools customers already use (SIEM, ticketing, cloud-native security).
Partner with engineering from discovery through launch and adoption — prioritizing, unblocking, and making the call when trade-offs are needed.
Set the product-craft bar for the team — exemplar specs, rigorous discovery, sharp prioritization, and crisp written decisions — and mentor other PMs through it.
Work with Product Marketing, Sales, and Customer Success to position your domain, enable the field, and close the loop from customer feedback to roadmap.
Use agentic AI tooling in your own practice — research, analysis, prototyping — as a disciplined force multiplier, with rigorous validation of anything that informs a decision.