Director, Analytics Engineering

Tebra

16h•$240,000 - $270,000•Remote

About The Position

We're looking for a Director of Business Analytics Engineering to lead and grow the data modeling function within our Business Data & Analytics organization — a player/coach who thrives at the intersection of technical depth and people leadership. You'll own the data models, transformation layer, and semantic foundation that power business decision-making, while building and mentoring a high-performing team of analytics engineers. This role sits at the core of how our internal business stakeholders — including Finance and the Data Analysts who support them — trust and use data. You'll partner closely with Finance-aligned Data Analysts to understand their analytical needs and translate those into clean, well-governed, reusable data assets. Our BI Engineering team owns the data ingestion pipelines and stack administration; your lane is the semantic layer and data models built on top of that foundation.

Requirements

7+ years of experience in data, with 3+ years in analytics engineering or a closely adjacent function.
5+ years of people management experience; you've built and developed teams, not just led them.
Deep, hands-on expertise with dbt — you've built production-grade dbt projects and can speak fluently to modeling patterns, testing, macros, and CI/CD.
Strong Snowflake proficiency and ecosystem — query optimization, warehousing strategy, cost management.
Experience partnering with Finance, FP&A, or business operations teams — you understand the domain and can speak their language as fluently as SQL.
Fluency with Tableau or comparable BI tools; you understand what good data modeling looks like from the consumer's perspective.
Track record of building and maintaining semantic/metrics layers and defining organizational standards for KPIs.
Strong communication skills — equally comfortable in a dbt PR review and a presentation to the VP of Finance.

Nice To Haves

Familiarity with or experience in SaaS business models and related metrics.
Experience with data observability tooling (Monte Carlo, Elementary, etc.).
Familiarity with data mesh, data products, or federated ownership models.
Exposure to ML feature engineering or working alongside Data Science teams.
Background in a high-growth or scaling analytics environment.

Responsibilities

Design and own a scalable, well-documented semantic layer that serves as the authoritative source of truth for business metrics and KPIs – the foundation Finance and Data Analysts rely on daily.
Establish data engineering best practices, modeling standards, and data quality controls across the organization.
Own and evolve our dbt + Snowflake transformation layer, including modeling standards, testing frameworks, documentation practices, and CI/CD workflows.
Drive data quality; build automated testing, alerting, and observability practices that give business stakeholders confidence in the numbers.
Partner with BI Engineering on the handoff between ingested data and modeled data, ensuring clean interfaces and clear ownership boundaries.
Ensure Tableau dashboards and Finance-facing self-service analytics are powered by clean, performant, well-modeled data assets.
Hire, develop, and retain a team of analytics engineers, setting clear expectations and career growth paths.
Foster a culture of craft — high standards for code quality, peer review, documentation, and knowledge sharing.
Partner with Finance leadership and their Data Analysts to deeply understand business data needs and translate them into the modeling roadmap.
Serve as a technical escalation point and hands-on contributor when the work demands it.
Serve as the primary data modeling and engineering partner to Finance and other internal business functions — translating analytical requirements into durable, reusable models that analysts can self-serve against.
Assist in establishing company-wide standards for metric definitions, data governance, documentation, and data stewardship across business functions.
Define the Business Analytics Engineering roadmap and communicate priorities and progress to senior leadership.
Champion data governance, lineage, and trust — ensuring business stakeholders always know which numbers to trust and why.
Collaborate on shared standards while maintaining clear ownership of the modeling and semantic layer domain.
Evaluate and adopt tooling and best practices as the business data ecosystem evolves.