CapIntel is a software platform built for wealth management enterprises to help financial advisors explain complex investment strategies to their clients. Advisors at some of the biggest banks across North America are winning trust by using CapIntel to easily compare investments and create compelling, educational presentations. Since launching in 2019,CapIntelhas seen rapid adoption and industry recognition, earning top placements in Deloitte’s Technology Fast 50 Canada and Rapid 500 North America in 2025, ranking us among the fastest -growing technology companies. As Technical Lead for the Data team at CapIntel, you'll own the platform that every CapIntel product depends on: the pipelines, workflows, and services that ingest, normalize, and serve financial instrument data and client portfolio data to advisors and institutions. You'll split your time roughly evenly between leading the team and building alongside it, and you'll be accountable for both the reliability of the platform and the growth of the people who run it. Durable workflow orchestration, standardized logging and metrics, continuous monitoring, and a quality‑first definition of done are now in place, and production incidents have become rare. Your job is to take that foundation to the next level: build a team that owns outcomes and can turn data reliability into a product advantage that advisors and investors actually feel. You'll work closely with the Data Product Manager, the other engineering team leads, Architecture, Client Solutions, and Implementation. You'll also be a go‑to resource for other teams adopting the workflow and observability patterns the Data team pioneered. This role is ideal for a senior data engineer who has already led a small team, prefers building leverage over being the hero,



and gets energy from raising the bar for everyone around them. Lead a team of data engineers day to day, matching people to work deliberately and planning capacity ahead of need rather than in reaction to it Grow ownership and technical leadership across the team, so delivery, client onboarding, and production support do not depend on any single person, including you Run performance management with clear expectations, and timely actions Own the architecture and health of the Data platform: serverless services, batch and streaming pipelines, legacy pipelines, search indexes, and durable workflow orchestration Drive the evolution toward an enterprise‑grade data architecture, covering data contracts, lineage, data quality checks, and DataOps practices Build systematic, proactive monitoring of data flows so data quality issues are caught before a client or advisor sees them Keep the quality bar high through design review, code review, testing discipline, and a definition of done the whole team holds each other to Own client data onboarding and implementation work for the Data team, making it faster and more predictable for clients and other teams. Partner with Product to translate client and advisor needs into data capabilities that create visible, positive moments for users, not just a platform that stays up Contribute hands‑on code to the platform, particularly on new systems, high‑risk changes,



and areas that need a reference implementation Measure and communicate the team's outcomes (reliability, data quality, onboarding time, delivery predictability) so leadership, other teams, and clients can see the impact Coach other teams on the workflow, observability, and data patterns the Data team owns, and contribute standards that other teams adopt because they work Use AI tooling as a core part of how the team works, from development and review agents to internal knowledge tools, and help define how the team uses AI safely on financial data 7+ years of professional software engineering experience, with at least 4 years focused on data engineering, data platforms, or ETL in production ~2+ years leading engineers as a team lead or tech lead, including hiring, performance management, and growing others into technical leadership ~ Deep, current Node.js and TypeScript experience. Python experience is a welcome addition alongside it ~ Deep experience designing and operating data pipelines on AWS, including serverless architectures ~ Strong SQL and experience with relational databases and search technologies (for example PostgreSQL and OpenSearch or Elasticsearch) ~ A track record of building observability into data systems (structured logging, metrics, dashboards, alerting) and using it to reduce incidents ~ Experience making engineering outcomes measurable and visible to non‑technical stakeholders ~ Bonus Experience Experience with financial instrument or market data and the reconciliation challenges that come with it Experience in a regulated industry and familiarity with data privacy obligations such as PIPEDA Experience with DataOps practices, data contracts, data lineage, or data quality frameworks Experience

📌 Technical Lead, Data (Ottawa)
🏢 CapIntel
📍 Ottawa