Data Platform Lead

Convene Hospitality Group

1d•$180,000 - $200,000

About The Position

Convene Hospitality Group is hiring a hands-on Data Platform Lead within its Technology organization. The role owns the data platform end to end, from architecture and reliability to business modeling and roadmap, while managing and growing analytics engineers. It will modernize the platform and support AI-first development in partnership with product engineering teams. The Lead will move existing source-shaped marts toward domain-oriented models that reflect how the business operates, and establish governed dashboards, shared metrics, and conversational analytics. The current platform uses Snowflake, dbt, and Fivetran, with plans to build on Snowflake native capabilities, roll out ThoughtSpot, and address gaps such as observability. The role includes managing platform configuration as code with Terraform, establishing data governance and quality practices, and enabling trustworthy analytics for business users and AI agents. This is a hands-on management role open to first-time managers, with coaching from the VP of Engineering. Experienced managers who want to continue building are also welcome. The role is expected to involve substantial hands-on work, including writing dbt models, reviewing pull requests, debugging pipelines, and prototyping approaches.

Requirements

7+ years in data engineering, analytics engineering, or data architecture, including building a modern data platform used in production.
Use AI agents for real multi-step work such as building models, writing tests, debugging pipelines, and documenting systems; provide clear specifications and context and verify agent output against the real system.
Deep experience with dbt and Snowflake, including project structure, testing strategy, and warehouse performance at scale.
Experience managing a platform as code with Terraform or a similar tool.
Fluency in data modeling and experience moving a team from source-shaped models to business-shaped, domain-oriented marts and driving adoption.
Experience building data quality and observability practices, including tests, contracts, freshness monitoring, and trusted alerting.
Experience building a semantic layer and governed metrics, with understanding of the requirements for accurate natural-language and agent-driven analytics.
Experience with managed ingestion such as Fivetran and understanding of its appropriate uses and limitations.
Leadership beyond individual work, such as serving as a tech lead on a multi-person project, mentoring engineers, running interview loops, or directing contractors or vendors.
Clear communication with technical and nontechnical audiences, including explaining modeling decisions to analysts and metric definitions to CFOs.
High agency: able to set direction, make architecture decisions, and document reasoning without waiting for a specification.

Nice To Haves

Experience rolling out ThoughtSpot or another search-driven BI tool.
Experience deploying an autonomous agent and building the guardrails, monitoring, and improvement loop around it.
Experience modeling data from Salesforce, NetSuite, or HRIS platforms.
Experience preparing data for AI use cases such as retrieval, agents, or feature pipelines.
Experience in hospitality, events, real estate, or another customer-centric, multi-location business.

Responsibilities

Own the data platform architecture, reliability, and roadmap across Snowflake, dbt, and Fivetran.
Move platform configuration from the UI into Terraform, coordinating with Cloud Infrastructure so warehouses, roles, grants, and connectors are versioned, reviewed, and reproducible.
Move from source-shaped marts to domain-oriented business models; establish modeling standards, naming conventions, and review practices.
Partner with Finance, Sales, Operations, and venue leadership to translate business questions into trusted models, metrics, and definitions.
Partner with product engineering teams on schema design, data contracts, and upstream changes.
Establish data quality and observability practices, including testing standards, contracts, reconciliation checks, and alerting.
Own the metrics and BI layer, roll out the BI tool, and build a semantic layer using Snowflake semantic views, certified metrics, and shared definitions.
Enable natural-language querying and agents using Snowflake native tools, with evaluations and guardrails to keep agent, dashboard, and human answers aligned.
Govern analytics and AI data through access controls, PII handling, and accessible documentation.
Manage and hire analytics engineers and direct contractors; coach through code review, pairing, and clear expectations.
Remain hands-on by writing dbt models, reviewing pull requests, debugging pipelines, and prototyping approaches.