VP, Data + Information Management

The VP, Data & Information Management is the architect and owner of Pave America's enterprise data layer, master data management practice, and the data foundation that makes applied AI and ML possible at scale. This is a greenfield build at a PE platform company spanning three deliberately balanced disciplines: (1) Master Data Management - the canonical record of customers, vendors, jobs, assets, employees, and chart of accounts across all acquired brands, with the golden-record / merge-survivorship logic that turns all disparate source systems into one trustworthy enterprise view; (2) Data Modeling & Semantic Architecture - a Snowflake-based Enterprise Data Warehouse built on disciplined dimensional modeling, a Kimball-style mart layer, governed dbt project architecture, a semantic layer in Power BI (or equivalent), and the Direct Margin (DM) formula codified consistently across PavementSoft and NetSuite; (3) Applied AI / ML Data Platform - the active ownership of feature stores, label pipelines, training data quality, and ML evaluation infrastructure that VP Innovation's ML team consumes. The VP, Data is not the consumer of AI requirements - the role is the active builder of the data foundation that determines whether AI at Pave succeeds or stalls. Outputs feed branch-level P&L, operational KPIs, M&A integration, AI use cases, and AEA board reporting from a single source of truth.

• Master Data Management: Own the MDM practice end-to-end across all brand entities - golden-record design and merge-survivorship logic for customer, vendor, job, asset, employee, and chart-of-account dimensions; MDM tool selection (Reltio / Profisee / Informatica MDM / Tibco EBX or the right-sized alternative for Pave); data stewardship operating model with named stewards in Finance, Ops, and HR; the MDM-vs-EDW boundary; data quality scoring and remediation cadence; and the explicit role MDM plays in M&A integration (Day-1 customer dedup, vendor consolidation, asset onboarding).
• Data Modeling & Semantic Architecture: Lead the dimensional modeling and semantic architecture of the EDW - staging / intermediate / mart layering inside dbt; Kimball-style fact and conformed-dimension design; slowly-changing-dimension handling (SCD Type 1/2/4 patterns); explicit data contracts between source systems and the warehouse; naming conventions, model documentation, and lineage discipline; semantic-layer governance in Power BI or equivalent that prevents metric drift across consumers. This is the discipline that determines whether the EDW scales gracefully or rots over five years.
• Direct Margin (DM) formula standardization: Own the end-to-end Direct Margin formula across PavementSoft and NetSuite; partner with the CFO and the NetSuite implementation partner to enforce a single canonical DM definition; encode it in the semantic layer and reconcile the variance to <1% between source systems.
• EDW design & build: Lead the Snowflake (or Redshift) + dbt + Power BI + reverse ETL stack build, managing the SI partner. Stand up source feeds from PavementSoft, NetSuite, HubSpot, Paycor, Samsara, and Limble with documented data contracts and validated pipelines.
• Applied AI / ML Data Platform: Actively own the data foundation that makes ML possible - feature stores, label pipelines, training data quality, point-in-time correctness for time-series features, ground-truth instrumentation, and ML evaluation infrastructure. Establish the clean hand-off boundary with VP Innovation: VP Data owns the data foundation (features, labels, training sets, evaluation pipelines, monitoring); VP Innovation owns the models (training, tuning, deployment, productization).
• KPI framework: Deliver the VCP-linked KPI framework (revenue, DM, EBITDA, crew productivity, bid-to-actual variance, cash conversion, working capital) with clear ownership, refresh cadence, and a single canonical definition encoded in the semantic layer - not in a hundred Excel files.
• BI & self-service analytics: Roll out Power BI (or equivalent) on top of the governed semantic layer - branch GM dashboards, regional rollups, ELT operating-rhythm packs, and AEA board reporting. Drive a culture of "data in the semantic layer, dashboards on top, no spreadsheet shadow systems."
• Source-system reconciliation: Design and operate the reconciliation architecture between PavementSoft, NetSuite, HubSpot, Paycor, Samsara, and Limble. Variance reporting visible to the CFO weekly during the close.
• Team build: Hire and lead Data Engineer(s), Analytics Engineer (dbt-focused), Data Governance & MDM Analyst, BI Lead, and the SI partner engagement (~3-4 FTE + SI at maturity in FY26).

• MDM practice live with golden-record logic in production across customer, vendor, job, asset, employee dimensions; data stewards named in Finance, Ops, and HR; data quality scoring published
• Dimensional model documented with Kimball-style fact/dim design, dbt staging/intermediate/mart layering enforced, naming conventions and data contracts in version control
• Semantic layer in Power BI (or equivalent) with single canonical DM, revenue, and EBITDA definitions - no metric drift across consumers
• Snowflake EDW in production with PS + NetSuite + HubSpot + Paycor + Samsara + Limble source feeds
• DM formula codified and reconciled between PS and NetSuite with <1% variance
• Feature store(s) and label pipelines live for the first VP-Innovation ML use cases (AI bid generation, pricing, or property condition scoring)
• Power BI rolled out to all all branches with branch-level P&L, crew productivity, and bid-to-actual dashboards
• MOR automation - monthly operating review pack generated from EDW (replacing manual Excel builds)
• AEA board reporting pack generated from EDW with no manual reconciliation
• Data dictionary, lineage, governance policies, and SCD handling standards published and version-controlled

• 10+ years data leadership with 4+ years as Head of Data / VP Data at a PE-backed or multi-entity business
• Hands-on experience building a greenfield Snowflake (or BigQuery / Databricks) + dbt EDW - including the staging / intermediate / mart layering and the data contracts that hold them together
• Deep dimensional modeling expertise - Kimball / Inmon, conformed dimensions, slowly-changing-dimension patterns, semantic layer design - sufficient to architect from first principles, not just pattern-match
• Direct ownership of an MDM practice at multi-entity scale - golden-record design, merge-survivorship logic, MDM tool selection (Reltio / Profisee / Informatica or equivalent), stewardship operating model
• Active ownership of feature stores, label pipelines, and ML training data foundations - not just downstream analytics; demonstrated partnership with an applied ML team
• Deep fluency in financial data models - GL integration, revenue recognition, job costing, DM / gross margin attribution
• NetSuite data model expertise
• Proven semantic-layer + BI rollout (Power BI, Looker, Tableau) at 1,000+ employee scale
• Vendor / SI management experience delivering an EDW on time and on budget

• Field-service or construction-tech data models - job costing, WIP, percentage-of-completion, multi-entity intercompany
• dbt certification; Snowflake SnowPro Advanced; experience with semantic-layer products (dbt Semantic Layer, Cube, LookML)
• Direct experience with reverse ETL (Hightouch, Census) for operational analytics activation
• Prior collaboration with Big-4 consulting on PE-grade data workstreams
• Hands-on experience with a feature store (Feast, Tecton, Databricks Feature Store) and ML evaluation infrastructure (Weights & Biases, MLflow, evaluation harnesses)

Remote-first (U.S.). ~25% travel for branch data discovery, and leadership offsites.