VP of Data

1 мес. назад

USAC-levelRemote

data transformationdata management

Lead company-wide data transformation and establish data as a key asset.

О компании

is on a mission to empower people to open and manage doors & gates from a smartphone. Our products are installed in more than 20,000+ multifamily, commercial, gated communities, and student-housing properties worldwide, including properties developed, owned, and managed by the most trusted names in real estate. Our features are designed for developers, owners, property managers, and tenants and our products lower operating costs and improve tenant satisfaction.

Обязанности

Define and execute ’s enterprise data strategy.
Prioritize transformation work based on commercial impact, decision value, risk, and organizational leverage.
Balance foundational investments with rapid delivery of visible business value.
Establish the governed data layer, primarily centered on Snowflake, as ’s authoritative source of truth.
Ensure leaders can clearly understand performance, diagnose problems, identify root causes, and act confidently.
Translate business questions into durable analytical systems rather than one-time analyses.
Partner with Finance, Sales, Marketing, Customer Success, Product, and Operations to define the economic and operational relationships that drive company performance.
Identify leading indicators and causal relationships, not merely lagging reports.
Create mechanisms that reveal emerging risks and opportunities before they become obvious in monthly or quarterly reporting.
Improve planning, forecasting, resource allocation, prioritization, and accountability throughout the organization.
Establish a company-wide metric governance framework.
Treat data products with the same rigor applied to strong external software products. Define users, use cases, adoption goals, trust requirements, service levels, documentation, and success measures for each important data product.
Design data experiences that are intuitive, discoverable, and easier to use than informal alternatives.
Measure adoption, usability, reliability, consumer satisfaction, and decisions influenced.
Build a data organization in which AI agents are a primary working tool, not an experiment or side project.
Remain technically engaged enough to review code, inspect models, prototype solutions, and participate directly when difficult problems require senior judgment.
Engineer data quality into the platform rather than relying on manual review and reconciliation.
Centralize standards, architecture, governance, and core data products while decentralizing responsible usage.
Develop strong partnerships with every major business function.
Lead, develop, and expand a high-performing data organization, beginning with four

Требования

The strongest candidates will demonstrate the following qualities.
12+ years of progressive experience across data engineering, analytics engineering, business intelligence, data architecture, data products, or related disciplines.
5+ years leading professional data teams, including experience managing managers or senior technical leaders.
Demonstrated success building or transforming a data function within a growing SaaS or technology company of comparable scale.
Deep experience with modern cloud data platforms, preferably including Snowflake.
Strong experience with data transformation and modeling frameworks such as dbt.
Experience with managed ingestion platforms such as Fivetran and with designing reliable source-to-warehouse pipelines.
Strong experience with business intelligence and self-service analytics platforms such as Sigma.
Deep understanding of dimensional modeling, semantic layers, entity resolution, master data, golden records, data contracts, lineage, observability, and quality engineering.
Experience integrating and governing data from Salesforce and other core SaaS business systems.
Demonstrated ability to establish governed business metrics and resolve cross-functional definition disputes.
Strong commercial understanding of recurring-revenue business models and SaaS operating metrics.
Experience designing data products for executive, operational, analytical, and machine consumers.
Experience supporting planning, forecasting, customer lifecycle analysis, go-to-market analytics, financial reporting, and product analytics.
Personally built and shipped at least one production AI agent or agentic workflow.
Strong understanding of AI-agent evaluation, tool access, context management, observability, permissions, security, and operational reliability.
Hands-on technical capability to write and review SQL, inspect dbt projects, evaluate models, troubleshoot pipelines, and prototype solutions.
Exceptional written and verbal communication.
Demonstrated ability to influence senior executives and drive difficult cross-functional changes.
Bachelor’s or advanced degree in a relevant technical, quantitative, or business field is welcome but not required. Exceptional experience, judgment, and results matter more than credentials.

Условия

The expected base salary range for this position is $250,000 - $300,000. Actual compensation will depend on factors including budget, skills, experience, location, and internal equity. This position may also be eligible for bonuses, equity, or other forms of compensation, where applicable.
Comprehensive Medical, Dental and Vision plans ( covers 80% of the cost) starting day 1
401(k) plan with a match
10 paid holidays, 20 vacation days, 5 sick days, 3 floating holidays
Basic Life and Accidental Death and Dismemberment Insurance ( covers 100% of the cost)
Short and Long Term Disability ( covers 100% of the cost)
Paid Family Leave
Employee Assistance Program
Quarterly self-care stipends
Access to optional benefits including pre-tax flexible healthcare spending accounts (FSA and HSA), Dependent Care FSA, and Commuter Benefits, as well as optional Supplemental Life, AD&D, Hospital Indemnity, Legal, Accident, Critical Illness, Pet, and Personal Liability Insurance
And more!
is an equal opportunity employer and we value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. You must have the authorization to work in the US to become an employee. We strive to create an accessible and inclusive experience for all candidates and employees. If you need reasonable accommodations during the application or the recruiting process, please let our recruiting team know.

Другое

Developers and owners no longer need to run building wiring or install in-unit hardware. Property managers can grant building access, revoke permissions, and review entry logs from an online dashboard. Residents can open doors from their smartphones, issue visitor access, and see who is trying to enter the building.
Fantastic people are the key to our success. As a distributed, primarily remote workforce, we’re looking for more intelligent, passionate, collaborative, ai-forward, and down-to-earth individuals to join our growing team. We’re driven by a shared commitment to excellence and innovation, grounded in our core values: We delight our customers, We take ownership, We are a community of collaborators, We speak up, We think big and do small, and We are tenacious.
is looking for a world-class Vice President of Data to lead a company-wide data transformation and establish data as one of our most important operating advantages.
We have invested in the right foundational technologies, including Snowflake, Fivetran, dbt, Sigma, and modern AI platforms. However, technology alone does not create a strong data organization. We need an exceptional leader with the vision, judgment, technical credibility, commercial fluency, and organizational influence to turn these systems into a trusted operating picture of the business.
The VP of Data will establish Snowflake and the governed data layer as the company’s source of truth. Every important business entity will have a trusted golden record. Every critical metric will have one definition, one owner, and one official home. Executives, operators, dashboards, workflows, and AI agents will use the same authoritative data products.
This leader will enable every function to understand performance, diagnose problems, identify opportunities, and make confident decisions faster. They will not measure success by the number of dashboards produced. They will measure it by the quality and speed of decisions, the adoption and trust of data products, and the degree to which data multiplies the effectiveness of our people.
This is a highly visible leadership role reporting directly to the CTO, with four initial direct reports and broad influence across Product, Engineering, Sales, Marketing, Customer Success, Finance, Operations, and Executive Leadership.
The role requires an unusual combination of diplomacy and conviction. The VP of Data must build strong partnerships across the company, understand the legitimate needs of each function, and navigate disagreement without becoming aligned to any single political constituency. At the same time, this person must be willing to challenge leaders, reject weak definitions, expose inconvenient truths, and push persistently for the changes required to establish trusted and durable systems.
We are not looking for a conventional reporting leader, an enterprise data bureaucrat, or someone who operates only at the strategy level. We need a hands-on executive who has built at our scale before, can challenge architecture decisions, understands the business deeply, and remains close enough to the work to inspect models, write code, prototype agents, and help solve difficult problems personally.
You have a clear point of view about what a world-class data organization should become.
You can translate that vision into an executable sequence of systems, products, operating changes, and organizational decisions.
You have led meaningful transformation rather than merely maintaining a mature environment.
You can create momentum while building foundations that will endure.
You know how to distinguish transformative priorities from attractive distractions.
You speak the language of the business, including ARR, bookings, retention, expansion, churn, pipeline, conversion, payback, margin, productivity, and unit economics.
You understand how SaaS businesses create and lose enterprise value.
You can connect data investments to revenue, cost, customer outcomes, risk, and organizational effectiveness.
You can challenge a business assumption as credibly as you can challenge a data model.
You are interested in decisions and economics, not just SQL and dashboards.
You possess deep knowledge of modern data architecture, engineering, analytics engineering, governance, quality, and business intelligence.
You can challenge architecture decisions and identify weak technical reasoning.
You remain capable of writing code, reviewing models, debugging pipelines, and prototyping solutions.
You are senior enough to set strategy but have not become detached from implementation.
You understand when technical simplicity is strength and when additional rigor is required.
You have built or transformed a data function at a company with comparable scale, complexity, and growth dynamics.
Your experience is not limited to either an early-stage startup with little operating complexity or a large enterprise with abundant specialization and resources.
You know how to sequence investments when the company needs better answers immediately but foundational work is incomplete.
You have operated through ambiguity, imperfect systems, limited capacity, and competing priorities.
You build processes that are disciplined without being cumbersome.
You use AI agents as a normal part of your own daily work.
You have personally built and shipped at least one production agent that created meaningful value.
You understand agent architecture, tool use, context, evaluation, observability, permissions, failure modes, and human oversight.
You can identify workflows that are genuinely improved by agents and reject those that are not.
You know how to turn experimental prototypes into dependable operational capabilities.
You treat datasets, metrics, semantic models, dashboards, and agent interfaces as products.
You care about users, adoption, discoverability, usability, trust, support, versioning, and deprecation.
You understand that shipping a dashboard is not the same as changing a decision.
You simplify the experience of using data rather than transferring technical complexity to consumers.
You measure whether the product achieved its intended business outcome.
You can navigate disagreement among functions without becoming a weapon for any one group.
You understand that metric disputes often reflect real differences in incentives, workflows, definitions, and decision needs.
You listen carefully, identify the underlying issue, and facilitate principled resolution.
You build relationships without sacrificing independence.
You can tell an executive that their preferred interpretation is unsupported while preserving trust and forward progress.
You know when to compromise on implementation and when not to compromise on truth.
You are willing to disagree with the room when the evidence disagrees.
You distinguish confidence from certainty and make assumptions explicit.
You do not manipulate definitions or analyses to produce a desired answer.
You communicate inconvenient findings diplomatically but directly.
You are relentless about getting to the truth while remaining open to being wrong.
You demonstrate sound judgment, integrity, and intellectual honesty even when doing so is difficult.
You can build broad support for difficult changes.
You are empathetic toward legitimate stakeholder constraints without allowing them to become permanent excuses.
You know how to push hard without becoming needlessly adversarial.
You can absorb disagreement, remain composed, and continue driving toward the right outcome.
You create clarity, establish ownership, and follow through until change is implemented.
Strong candidates should be prepared to discuss specific examples of:
A data transformation they personally led, including the starting condition, resistance encountered, architecture chosen, sequence of work, and measurable business results.
A source-of-truth or golden-record initiative they implemented across conflicting systems.
A metric-definition dispute involving multiple executives or functions and how they resolved it.