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# Senior Analytics Engineer

- **Company:** EF Education First
- **Location:** Boston, MA, United States
- **Experience:** Expert
- **Salary:** $135,000.0 - $170,000.0
- **Contract:** Permanent contract
- **Skills:** Sql Data Warehouse, Artificial Intelligence, Data Analysis, Business Logic, Automation of Tests, Cloud Computing, Extract Transform Load (ETL), Google Analytics, Python (Programming Language), Raw Data, Standard Sql, Web Analytics, Large Language Models, Snowflake
- **Published:** October 7, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=5f805ac0743d5e2d

## About the Role

* Experience owning analytical data products that other people use to make decisions (typically 5+ years in analytics engineering, analytics, or a closely related data role).
 * Strong SQL and hands-on experience modeling in a cloud data warehouse (Snowflake preferred).
 * Hands-on experience with dbt (dbt Core and/or dbt Cloud), including models, tests, and documentation other people extend.
 * Experience defining shared metrics or a semantic layer that reporting, self-serve, or AI tools depend on.
 * A track record of partnering with analysts and business stakeholders, including reframing a request around the decision it is meant to inform.
 * Experience mentoring, reviewing, or raising the bar for other engineers or analysts.
 * Working proficiency in Python for data tooling and automation.
 * Bonus: reverse ETL / data activation (Hightouch), or product data that powers marketing and advertising.
 * Bonus: semantic layers or metric layers used by LLM-driven analytics or self-serve tools.
 * Bonus: digital analytics event data, especially Google Analytics (GA4): sessions, events, and models that connect web behavior to business outcomes. Bonus: measurement for experimentation, lifecycle, or marketing attribution.
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## Description

* Translate ambiguous business questions into trusted data products: modeled datasets, metric definitions, and documentation that analysts, product, and business partners can rely on.
 * Design and evolve our semantic layer so the same definitions power dashboards, self-serve analysis, and LLM-driven analytics.
 * Build and own dbt models that turn raw data into well-tested analytical datasets, with business logic defined once and reused across brands.
 * Model Google Analytics (GA4) and our other digital analytics into datasets marketing and product can trust: sessions, events, and on-site behavior, joined to how travelers discover, consider, and book a tour.
 * Partner with analysts and stakeholders to shape the question before you build. Bring the dataset that will change a decision, and say so when a request will not.
 * Activate trusted data into the tools the business already runs on, including marketing, CRM, and advertising: reverse ETL (Hightouch) and the product catalogs behind our advertising and personalization channels.
 * Turn recurring questions into something people can answer themselves: clear metrics, semantic models, and lightweight AI-assisted workflows, with you still accountable for the definition and the result.
 * Build data quality into everything you ship: automated tests, monitoring, and explicit ownership of the numbers.
 * Set the modeling and review bar for your domain, and mentor engineers and analysts who are building toward it. Contribute to the direction of our analytical layer: where we invest in shared models, where we enable self-serve, and which questions are worth a durable data product.
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How you work

 * You take ownership, from an unclear problem through a trusted data product in production.
 * You collaborate as a partner. You join discovery early, shape scope with analysts, product, and the business, and give and receive candid feedback.
 * You care about maintainability: clean models, thoughtful reviews, and documentation others can rely on.
 * You balance pragmatism with quality, knowing when a directional dataset should ship and when the definition needs to be right first. You use AI tools to work smarter, and you build the definitions and semantic foundations that let teammates and agents use them well.
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