Technical Lead, Data Team

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. This is a hands-on leadership role. 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.

The team has built a mature, observable platform. 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.

What You’ll Do

Lead the team

• 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
• Hire, onboard, and ramp new engineers so they are independent quickly, and handle transitions and handovers without disruption to clients

Own the platform

• 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
• Formalize and embed incident response for the team: on-call expectations, runbooks, severity classification, communication during incidents, and blameless post-incident reviews that actually change the system
• Keep the quality bar high through design review, code review, testing discipline, and a definition of done the whole team holds each other to

Deliver for clients

• 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

Multiply beyond the team

• 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

What We’re Looking For

• 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. This is a core requirement for the role; Python experience is a welcome addition alongside it
• Deep experience designing and operating data pipelines on AWS, including serverless architectures
• Hands-on production experience with Temporal.io, or a comparable durable workflow orchestration platform (for example Airflow, Dagster, or Step Functions) with a clear ability to ramp on Temporal quickly
• 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 leading incident response and running post-incident reviews that lead to lasting fixes
• Evidence that you’ve built a team that delivers without you: delegation, ownership, and a team that is better because you were there
• Experience making engineering outcomes measurable and visible to non-technical stakeholders
• Composure under pressure. You keep discussions constructive when systems, clients, or timelines are under stress

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 using AI-assisted development tools as an established part of your own and your team’s workflow, or building AI tools over internal data
• Familiarity with Datadog or similar observability platforms