Specialist - Data Governance

What are we building?

Hard Rock Digital is a fiercely focused team working to become the best online sportsbook, casino, and social gaming company in the world. We're building a deeply curious team with a passion for learning, operating, and building new products and technologies for millions of consumers. We're customer-obsessed — we care about each customer interaction, experience, behavior, and insight, and strive to ensure we're always acting authentically.

Rooted in the kindred spirits of Hard Rock and the Seminole Tribe of Florida, Hard Rock Digital taps a brand known the world over as the leader in gaming, entertainment, and hospitality. We're taking that foundation of success and bringing it to the digital space - ready to join us?

What's the position?

Hard Rock Digital's reporting lives across a range of tools and platforms, and this role owns the standard for what makes that reporting trustworthy and builds the modeled data layer where that standard is enforced: the definitions, dbt models and tests, and certification process that ensure the data behind every report means what it's supposed to mean.

In the first 12 to 18 months, you'll lead the migration of our reporting estate as the onramp to the role: assessing existing dashboards across Salesforce, Tableau, and ADAM, our Snowflake-native analytics platform, migrating what's eligible, and rebuilding it to a higher visualization standard on properly modeled data rather than dashboard-level logic. This work is how you build the institutional knowledge to govern the data well, not the whole of the job.

From there, data governance and the modeled layer underneath it become your core, ongoing function. The framework you build, and the dbt models that implement it, become the standard the wider BI Operations team builds against going forward. As it matures, dashboard builds and rebuilds become shared across the team rather than sitting solely with this role.

This role reports directly to the Senior Manager, Operations Strategy & Insights, and sits within BI Operations.

Responsibilities

Data governance and data modeling (ongoing, core function)

• Own the data governance framework for ADAM: standards, naming conventions, definitions, documentation, and the certification process that ensures new data added to the platform is accurate and trustworthy before anyone relies on it

• Build and own the certified data layer in dbt: the models, tests, and documentation that make governance a property of the warehouse itself rather than a document describing it

• Act as the certification gate for new data while the framework is being established: when a business team or analyst wants to add new metrics, dimensions, or tables to the reporting environment, you partner with the BI Ops team to review, validate, and certify them, implementing the checks as dbt tests wherever they can be automated, or flag what needs to be fixed before they can be trusted

• Maintain data lineage so anyone can trace where a number comes from, how it's calculated, and whether it's been certified — generated from the dbt project itself rather than kept as a separate catalogue that drifts

• Monitor the health of the data environment over time: use automated testing and source freshness checks to catch when certified data drifts from its defined behavior, flag upstream changes that affect downstream reporting, and maintain trust in the platform as it grows

• Partner with the data engineering team on schema changes, new source integrations, and pipeline updates that affect the reporting layer, owning the transformation layer that sits downstream of their pipelines

• Partner with BI Operations leadership to define how governance responsibility transitions to the broader team once the framework is established and proven

Migrating and building reporting (first 12–18 months)

• Evaluate reporting across all platforms in use — Salesforce, Tableau, and others — and determine the right destination and format for each based on underlying data structure and business need

• Migrate eligible reporting into ADAM, landing each report or dataset where it actually fits best rather than defaulting to a like-for-like rebuild, and building the dbt models underneath it so the business logic lives in the warehouse layer rather than in the dashboard

• Improve and consolidate as you go: spot overlap and redundancy across the reporting landscape before diving into individual migrations, so consolidation happens by design rather than dashboard by dashboard

• Build every migrated report to a best-in-class visualization standard, with clear documentation of what it shows, how each figure is calculated, and where the underlying data comes from

• Identify data gaps or modeling issues that stand in the way of a clean migration — resolving in dbt what belongs in the reporting layer, and translating the rest into specific asks the data engineering team can act on

• Run stakeholder review and sign-off on each migrated report before the legacy version is retired

• Recognize when a tool is being used for something it's not actually built for, and propose a better-fit alternative