Director of Data & Analytics

Extended Stay America•Charlotte, NC

12h•Remote

About The Position

The Director, Data and Analytics, is accountable for the delivery and ongoing operation of ESA's enterprise data platform. The role converts an approved platform roadmap into working, supported production capabilities, including ingestion from source systems, curated and modeled data across medallion layers, governed access, and the analytics assets the business runs on. Accountability does not end at go-live. This leader owns the platform in production, including its reliability, cost, data quality, and the technical debt the team creates. The team is small and senior by design. The Director leads a Senior Data Engineer and a Technical Lead who coordinates offshore engineering and works day-to-day with implementation partner and offshore engineers. Leverage in this role comes from engineering judgment and delivery discipline rather than headcount. The expectation is that this leader is in the detail with the team, participating in design reviews, release readiness activities, and root cause analysis, rather than collecting status and passing it upward. The technical environment is a Databricks Lakehouse on AWS, with Lakeflow declarative pipelines, medallion layering, Kimball dimensional and data vault modeling, Unity Catalog governance, and Tableau for business intelligence. A legacy Snowflake environment supports private equity sponsor reporting and is under evaluation for consolidation. A multi-year platform program is underway, integrating ADP, central reservation system APIs, S3 file delivery, business-maintained spreadsheet feeds, and master data. ESA is actively applying artificial intelligence to how the data team works and to how the business reaches data. Current and near-term work includes AI-assisted engineering and code review, agentic development patterns, automated data quality detection, and natural language access to governed data through Databricks Genie. This is offered as something the person in this role will get to shape and direct. Prior AI experience is not expected and is not a requirement of the position. Interest is enough.

Requirements

Professional written and verbal communication and interpersonal skills, including the ability to hold and explain technical positions in front of executive stakeholders and to state plainly when something will not work.
Sound technical judgment, including the ability to review a data model, a pipeline design, or a query and articulate a defensible opinion on it.
Evidence-driven decision making, including forming a hypothesis, instrumenting, measuring, and changing position when the measurement disagrees.
Personal organization sufficient to hold a multi-workstream delivery plan accurately in view, including dependencies, risks, and commitments made to stakeholders.
Demonstrated ownership mindset, having supported and evolved platforms well beyond initial delivery.
Ability to move between executive framing and implementation detail within the same conversation.
Proactive engagement, including raising issues, bringing recommendations, and taking accountability without being prompted.
Collaborative working style with a strong internal customer focus.
Ability to lead and collaborate effectively in a fully remote, distributed, multi-time-zone environment.
Ability to facilitate design reviews, planning sessions, and incident reviews.
Bachelor's degree in Computer Science, Information Systems, Engineering, or a related field, or equivalent work experience.
Ten or more years of professional experience in data engineering, data platform delivery, or analytics engineering.
Four or more years leading teams that build and operate production data platforms.
Multi-year accountability for a production data platform the candidate's team built, including the period after launch, operating it, supporting it, absorbing its failures, and paying down its technical debt.
Demonstrated depth on a modern cloud data platform, sufficient to reason about the separation of compute and storage, table and file layout, incremental and idempotent loading, and cost-to-performance tradeoffs. Databricks is the current foundation, and equivalent depth on another platform is fully acceptable where the underlying fundamentals transfer, as detailed below.
Dimensional modeling depth, including the ability to define grain, explain how a fact table built at the wrong grain silently double counts, and select and defend a slowly changing dimension strategy. Working knowledge of data vault, including when it is the wrong choice.
Strong SQL, including the ability to read an execution plan and diagnose a poorly performing query.
Working fluency in Python is sufficient to read the team's pipeline code, review it credibly, and debug it.
Experience delivering integrations across heterogeneous sources, including vendor APIs, scheduled file delivery, SaaS extracts, and business-maintained spreadsheets, and experience with upstream source systems that change without notice.
Experience owning release management and production readiness for a data platform, including environment promotion, regression testing, cutover planning, rollback, and post-release stabilization.
Experience leading distributed teams that include offshore or partner engineers, with personal accountability for the quality of their output.
Experience managing implementation partners or vendors against statement-of-work scope, deliverable quality, and rate.
Experience building and defending a capacity plan and managing delivery within an approved budget.
Platform equivalency. We hire fundamentals, not vendors. A candidate who can reason about layered data architecture, governed access, production pipeline reliability, and dimensional or data vault modeling on Snowflake, Microsoft Azure (Synapse, Data Factory, Purview), or a mature SQL Server and SSIS estate is a qualified for this role. Platform syntax is not the point; underlying engineering judgment is.

Nice To Haves

Intellectual curiosity and genuine interest in emerging technology, particularly the practical application of artificial intelligence to data engineering and analytics work.
Coaching and mentoring skills, with a track record of growing engineers rather than routing work around them.
Creative thinking, problem solving, and comfort making decisions with incomplete information.
Comfort operating on a small team where individual contribution has visible impact.
General IT knowledge across infrastructure, information security, and enterprise applications.
Domain familiarity in hospitality, real estate, or multi-unit operations.
Prior experience at Director level or with equivalent delivery accountability on a small, senior team where the leader remained technically current.
Databricks-specific depth, including Lakeflow or Delta Live Tables, Unity Catalog, Delta Lake optimization, cluster and warehouse policy, and cost governance.
Experience consolidating or retiring a legacy analytics platform onto a single strategic platform.
Master data management experience.
Tableau or comparable business intelligence platform delivery, including semantic layer design and certified dataset practice.
Hospitality, hotel, or multi-property operations data experience.
Experience supporting private equity sponsor or lender reporting requirements.
ADP or comparable human resources and payroll data integration experience.
Demonstrated interest in applying artificial intelligence to data engineering practice, including AI-assisted development, agentic development patterns, automated data quality, or natural language access to governed data. This is an interest we want to hear about, not experience we require.

Responsibilities

Converts the approved data platform roadmap into a sequenced, resourced, estimated delivery plan, and maintains that plan accurately enough to state at any time what is on track, what is at risk, and what the recovery path is, without preparation time.
Owns end-to-end delivery of platform releases from design through production readiness, cutover, and stabilization.
Personally participate in release readiness and go / no-go reviews, confirm that acceptance criteria were met as written, and hold a release when they were not.
Maintains sprint discipline across internal, offshore, and partner resources, including a groomed and estimated backlog, committed sprint scope, visible progress, and honest reporting of carryover.
Verifies that delivered work matches the design intent rather than accepting completion at face value, and drives rework where it does not.
Participates directly in solution design and design review for ingestion patterns, medallion layer boundaries, dimensional and data vault models, orchestration, and approve or reject designs with stated reasoning.
Reviews data model changes, pipeline designs, material code changes, and defend the position taken in review.
Leads root cause analysis on production data incidents through to documented findings and corrective actions rather than to a restart.
Establishes and enforces engineering standards covering source control, environment promotion, testing, idempotent and re-runnable loads, deployment automation, and documentation.
Makes platform performance and cost decisions on measured evidence, including compute sizing, job scheduling, storage and file layout, cluster policy, and be able to show the measurement behind the decision.
Owns the platform in production including pipeline reliability, data freshness against agreed service levels, incident response, and remediation of accumulated technical debt.
Serve as the escalation authority for production data incidents, including outside standard business hours when severity requires it.
Maintains an accurate operational picture of what is running, what is failing, what is degraded, and what the platform is costing.
Defines and operates the data quality program, including expectations at each layer, automated validation, measurement against targets, published quality metrics, and a triage path for failures.
Owns operational governance of the platform, including the Unity Catalog access model, data classification and sensitive data handling, lineage, and certification of curated datasets for business use.
Partners with enterprise governance, information security, audit stakeholders on policy, and implement that policy in the platform.
Leads, coaches, and develops the onshore team, including recruiting, setting expectations, giving direct feedback, and managing performance.
Establishes effective working overlap and collaboration with offshore and partner engineers and holds partner deliverables to the same standard as internal work.
Manages implementation partner and vendor performance against statement-of-work scope, deliverable quality, rate, validate invoices against contracted terms, and escalate variance.
Owns capacity and resource planning for the data platform, builds the forecast that supports the annual technology plan, and manages delivery within approved spending.
Represents the data platform to business, finance, and executive stakeholders, bringing positions and recommendations rather than waiting to be asked, and communicating problems early.
Translates business questions into platform requirements and push back with reasoning on requests that the underlying data will not support.
Contributes to and help shape the practical application of AI within the team's engineering practice and in how the business reaches data.
Participate in enterprise architecture review and technology standards discussions.
Support audit, information security, and compliance requests related to the data platform.
Provide input to technology vendor selection and contracting for data platform capabilities.
All other duties as needed or required.