Data & Analytics Engineering Manager

Understood•New York, NY

4d•$185,000 - $200,000•Hybrid

About The Position

About Understood Understood is a nonprofit focused on shaping the world for difference. We raise awareness of the challenges and strengths of people who learn and think differently. Our resources help people navigate challenges, gain confidence, and find support and community so they can thrive. Having a shared commitment to our values is a key factor in any hire we make. We have five core values: Continuously learn. Act with intention. Champion difference. Inspire change. Grow together. Come be part of an organization with an entrepreneurial spirit that’s helping to shape the world for difference. Together, we can build a world where everyone can reach their full potential. To learn more about Understood, please visit: www.understood.org. Who you are You're a hands-on data leader who gets energy from driving efficiency, improving delivery speed, and raising the bar for technical quality. You've built and operated production data pipelines and models yourself, but you know your highest leverage is setting direction, prioritizing effectively, and clearing roadblocks for your team. You're equally strong at coaching senior engineers toward broader ownership and developing early-career engineers' fundamentals. You challenge conventional thinking, helping engineers adopt more agile and iterative ways of working. Above all, you care deeply about ensuring the business has the reliable, timely, and actionable data it needs to succeed. This is a hybrid role that requires a minimum of three (3) days a week in our NYC office, with a focus on Monday, Tuesday, and Thursday. We are a Mac-based environment, and team members are provided a Mac laptop for their work.

Requirements

In this role, you bring deep data expertise and the ability to own projects and workstreams end-to-end.
We don't expect a perfect match across every category. What matters is relevant experience, genuine interest in the work, and a mindset for growth
Experience: 7+ years of experience operating on a data team. You have owned projects or workstreams end-to-end. You have experience leading small teams.
People leadership and development: You are responsible for coaching, feedback, and day-to-day management. You have 2+ years of demonstrated success in managing data teams, including teams with a mix of senior and early-career engineers.
Stakeholder and cross-functional partnership: You partner directly with other teams and build trust with partners through clear communication and reliable delivery, including a proven ability to partner directly with senior executives.
Domain expertise: Subject-matter expertise in data and analytics engineering, including experience with the modern data stack -- data modeling in dbt, testing, orchestration, and CI/CD -- and proficiency in SQL and Python.
Strategic thinking and execution: You own projects and workstreams, translate team priorities into plans for your team’s work.
Project management: You plan and run projects and workstreams end to end using project management tools like Jira. You can set milestones, manage risk, and coordinate the work of contributors, including prioritizing work, running maintenance rotations, triaging data quality incidents, and managing SLAs.
Communication: You communicate clearly with teammates, your manager, senior leaders, and cross-functional partners, adapting to the audience.
Collaboration and ways of working: You take ownership and deliver high-quality work, proactively identify improvements to processes or workflows, and balance technical oversight with stakeholder alignment.
AI fluency: You apply AI tools to improve output and efficiency within owned projects. You evaluate where AI adds value in the work and share good practices with the team.

Nice To Haves

Familiarity with data governance for sensitive data, including PII handling and compliance frameworks such as HIPAA
Experience with A/B testing and experimentation platforms (we use Statsig)
Experience with event tracking and design on a platform like Snowplow

Responsibilities

Lead and grow the team. Manage a team of data and analytics engineers, from early-career to senior. Coach senior engineers toward broader ownership, build early-career fundamentals, and make sure every business domain has coverage.
Stay hands-on where it counts. Review code, pressure-test data models, and pair with engineers on hard problems while keeping your focus on direction, priorities, and clearing roadblocks.
Own the health of our data. Oversee domain-level pipelines, models, and data contracts to maintain quality and SLAs across our stack: dbt, dlt, Snowflake, Fivetran, Monte Carlo, Omni, and GitHub.
Run the operational engine. Own the maintenance rotation, data quality alert triage, and intake of ad-hoc data requests from across the organization.
Be our data modeling expert. Identify the dbt features we're not using enough, optimize orchestration and build performance, and set best practices in modeling, testing, and CI/CD.
Turn business needs into roadmaps. Partner with cross-functional stakeholders to prioritize work, set milestones in Jira, and deliver on commitments.
Connect the dots across domains. Spot dependencies early and keep definitions, metrics, and documentation aligned across teams.
Put AI to work in our workflows. Find where AI makes the team faster and more reliable. Maintain and improve the semantic layer that powers our self-serve and AI-assisted analytics.
Shape eventing and experimentation. Partner with Product and Engineering on event tracking design in Snowplow, and help evolve how we run A/B tests in Statsig.