Technical Lead, Data

CapIntel

About The Position

CapIntel is hiring a hands-on Technical Lead for its Data team. The role owns the platform that powers CapIntel products, including the pipelines, workflows, and services that ingest, normalize, and serve financial instrument and client portfolio data. The lead will divide their time roughly evenly between leading the team and contributing to its engineering work, with accountability for platform reliability and team growth. The Data team has established durable workflow orchestration, standardized logging and metrics, continuous monitoring, and quality-focused delivery practices. The Technical Lead will build on this foundation by strengthening ownership, proactively improving data reliability, and making the platform’s impact visible to advisors, investors, clients, and internal teams. The role works closely with the Data Product Manager, engineering leads, Architecture, Client Solutions, and Implementation, and advises other teams adopting the Data team’s workflow and observability patterns. The position combines people leadership, platform architecture and operations, client delivery, and hands-on software development. It includes developing an enterprise-grade data architecture, formalizing incident response, improving client onboarding, and helping establish safe and effective uses of AI tooling with financial data.

Requirements

At least 7 years of professional software engineering experience, including at least 4 years focused on production data engineering, data platforms, or ETL.
At least 2 years leading engineers as a team lead or tech lead, including hiring, performance management, and developing others into technical leadership.
Deep, current Node.js and TypeScript experience; this is a core requirement.
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, such as Airflow, Dagster, or Step Functions, and ability to ramp up on Temporal quickly.
Strong SQL skills and experience with relational databases and search technologies.
Track record of building observability into data systems using structured logging, metrics, dashboards, and alerting, and using it to reduce incidents.
Experience leading incident response and running post-incident reviews that produce lasting fixes.
Evidence of building a team that delivers without depending on the lead, through delegation and ownership, and of improving the team.
Experience making engineering outcomes measurable and visible to non-technical stakeholders.
Composure under pressure and ability to keep discussions constructive when systems, clients, or timelines are under stress.

Nice To Haves

Python experience.
Experience with financial instrument or market data and related reconciliation challenges.
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 personal and team workflows, or building AI tools over internal data.
Familiarity with Datadog or similar observability platforms.

Responsibilities

Lead data engineers day to day, deliberately assigning work and planning capacity ahead of need.
Develop ownership and technical leadership across the team so delivery, onboarding, and production support do not depend on any one person.
Set clear performance expectations and take timely performance-management actions.
Hire, onboard, and ramp engineers; manage transitions and handovers without disrupting clients.
Own the architecture and health of the Data platform, including serverless services, batch and streaming pipelines, legacy pipelines, search indexes, and durable workflow orchestration.
Evolve the platform toward enterprise-grade architecture, including data contracts, lineage, data quality checks, and DataOps practices.
Build proactive monitoring of data flows to identify quality issues before clients or advisors encounter them.
Formalize incident response, including on-call expectations, runbooks, severity classification, incident communications, and blameless post-incident reviews that lead to system improvements.
Maintain quality through design and code reviews, testing discipline, and a team-wide definition of done.
Own Data team client onboarding and implementation work, making it faster and more predictable.
Partner with Product to turn client and advisor needs into data capabilities that create positive user experiences.
Contribute code, particularly for new systems, high-risk changes, and reference implementations.
Measure and communicate team outcomes, including reliability, data quality, onboarding time, and delivery predictability.
Coach other teams on the Data team’s workflow, observability, and data patterns, and contribute standards that other teams adopt.
Use AI tooling in team workflows, including development and review agents and internal knowledge tools, and help define safe use with financial data.