Staff Analytics Engineer

HighLevel•Dallas, TX

13d•Remote

About The Position

We’re looking for a Staff Analytics Engineer to lead the definition, modeling, and governance of our enterprise data layer, which serves as the technical foundation that supports internal KPIs and investor reporting. This role owns the end-to-end technical standards for how data is modeled, tested, documented, and exposed across the company, ensuring that every number reported internally or externally is built on a consistent, auditable foundation. You’ll work at the intersection of data modeling, software engineering, and architecture, shaping the technical systems and conventions that keep our data accurate, governed, and verifiable from raw inputs through the datasets that support audits and disclosures.

Requirements

9+ years in data engineering, analytics engineering, or related roles with deep experience modeling data in dbt, Snowflake, or similar modern stacks
Proven ownership of an enterprise-scale data model or semantic layer used across multiple business functions
Advanced SQL and dbt skills; experience with CI/CD, testing frameworks, and Git-based workflows
Experience defining and enforcing data contracts, quality tests, and governance standards
Familiarity with SOX controls, audit evidence, or IPE lineage (experience in a public or IPO-bound company a plus)
Strong communication skills with the ability to translate between engineering, finance, and compliance stakeholders
Comfortable working in environments where precision, auditability, and trust in data are mission-critical

Responsibilities

Own the enterprise data model: Define and maintain canonical entities (Account, Customer, Location, Usage, Invoice, etc.) and their relationships across systems
Drive alignment between product, analytics, finance, and marketing data domains
Architect and maintain the dbt semantic layer: Build modular, tested, and versioned dbt models with rigorous standards for naming, documentation, and lineage
Manage exposures to ensure all metrics and dashboards trace back to tested sources
Govern KPI and metric definitions: Partner with Finance and BI to define and codify key company metrics (ARR, NRR, CAC payback, etc.) and enforce change control through versioned definitions
Enforce data contracts and schema governance: Define and validate schemas, event structures, and data types for all inbound systems
Implement CI/CD tests to block breaking changes and maintain cross-system consistency
Drive observability and data quality standards: Integrate dbt tests and freshness SLOs with the data catalog
Implement automated monitoring and alerting for data breaks and policy violations
Build the bridge between data and compliance: Collaborate with Legal, IT, and Internal Audit to ensure IPE (information produced by the entity) lineage, evidence retention, and SOX readiness
Mentor and multiply: Set technical direction and review standards in close partnership with the broader data organization
Define reusable macros, patterns, and documentation conventions that raise the bar for quality and reliability
Partner cross-functionally: Work closely with data engineering on ingestion and contracts, BI on dashboard alignment, and Finance on KPI integrity
Influence how new systems and features are instrumented at the source to keep the data layer consistent