Requirements:

8+ years of experience in data, analytics, or engineering roles with a strong record

of delivering measurable business impact through data.

● At least 3 years of experience leading data teams or projects, ideally as a

player-coach who combines strategic direction with hands-on execution.

● Strategic and hands-on leader who operates as a player-coach, setting data vision

while contributing directly (SQL, DBT, Looker). Leads by example rather than

delegation only. The platform and team are technically mature, so the role

weights toward data-to-business impact (roughly 70% data and analytics, 20%

people leadership, 10% platform engineering) rather than day-to-day systems

building.

● Deep practical expertise in modern data stack architecture, including:

- Segment for event tracking and data ingestion

- DBT for modeling, testing, and CI/CD

- BigQuery for large-scale data warehousing and cost-efficient query

optimization

- Looker (LookML) for semantic modeling and governed self-serve BI; familiarity

with Metabase (used for transactional and production-database dashboards)

is a plus

- Familiarity with Statsig, Mixpanel, or similar experimentation and product

analytics tools is a strong plus

- Comfort in a GCP-centric environment (also using Airflow and Spanner); AWS

or Databricks-based stacks are equally acceptable. Candidates limited to

closed or legacy systems only (e.g., Tableau-only or Oracle-only) are a weaker

fit.

● AI enablement (core requirement): demonstrable, hands-on use of LLMs within

a data product, and experience leading AI enablement rather than only

supporting it. This includes selecting and rolling out tooling that gives the

business a natural-language interface to the data warehouse and driving

self-serve adoption across teams (e.g., Cube, Hex, Looker AI/MCP, or similar).

Recent, continuous work in this space is expected given how quickly the tooling

has evolved.

● Strong governance mindset: experience establishing data dictionaries, contracts,

and quality standards that eliminate duplicate dashboards, metric drift, and

inconsistencies. Governance is a current priority, including financial-data

reconciliation and ongoing validation of data flowing through the system.

● Executive partnership and influence: able to engage with C-level and VP

stakeholders (Finance, Marketing, Product, Operations), manage competing

priorities, and translate business needs into a clear data roadmap. This role is the

primary interface between the data team and the rest of the business.

● Focus on enablement: builds scalable self-serve analytics and empowers product,

business, and engineering teams to use data confidently.

● Excellent communication skills in English, both technical and business, with the

ability to operate in a fully remote, global environment.

Nice to Have:

● Experience designing and running A/B tests or product experiments, including

sample sizing, significance, and event QA.

● Background in marketplace, SaaS, or platform businesses, ideally with exposure

to conversion, retention, and fraud/disintermediation metrics.

● Proven ability to coach and elevate mid-level data engineers and analysts toward

a stronger business-partner mindset.

● Experience leading teams that built and deployed ML models (e.g., Vertex AI on

GCP or SageMaker on AWS) and integrated them into the data stack, relevant to

future work such as host/cleaner matching, market-mix, and demand modeling