Senior Analytics Engineer

Join EF’s WoJo Engineering team in Boston to help build and operate the data platform behind customer journey analytics and AI/ML work. In this onsite role, you’ll create trusted analytical data products that turn ambiguous questions into reusable datasets, shared metric definitions, and production-ready models that marketing, CRM, advertising, product, and AI-driven analytics can rely on.

This is a Senior Analytics Engineer position with a salary range of $135,000 to $170,000 per year, based on experience. You’ll work at the intersection of data engineering and decision-making, with a focus on semantic layers, dbt modeling, data quality, and data activation into tools the business already runs.

What you’ll do

• Translate unclear business requests into trusted data products, including modeled datasets, metric definitions, and documentation for analysts, product, and business partners.
• Design and evolve the semantic layer so consistent definitions power dashboards, self-serve analysis, and LLM-driven analytics.
• Build and own dbt models that transform raw data into well-tested analytical datasets, with business logic reused across brands.
• Model Google Analytics (GA4) and other digital analytics into datasets for marketing and product, including sessions, events, and on-site behavior joined to how travelers discover, consider, and book a tour.
• Collaborate with analysts and stakeholders early to shape the question, set expectations, and deliver the dataset that changes decisions.
• Activate trusted data into business tools using reverse ETL (Hightouch), including the product catalogs behind advertising and personalization channels.
• Turn recurring questions into self-serve answers using clear metrics, semantic models, and lightweight AI-assisted workflows, while staying accountable for the definition and output.
• Embed data quality into delivery through automated tests, monitoring, and explicit ownership of the numbers.
• Set the bar for modeling and review within your domain, and mentor engineers and analysts working toward it.
• Contribute to the direction of the analytical layer, including where shared models live and which questions are worth durable data products.
• Use AI tools to work more effectively, building definitions and semantic foundations teammates and agents can use.

What you bring

• 5+ years of experience owning analytical data products used for decisions (analytics engineering, analytics, or a closely related 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 others can extend.
• Experience defining shared metrics and/or a semantic layer that reporting, self-serve tools, or AI tools depend on.
• A track record partnering with analysts and business stakeholders, including reframing requests around the decision they support.
• Experience mentoring, reviewing, or raising the bar for other engineers or analysts.
• Working proficiency in Python for data tooling and automation.

Tools you’ll work with

• SQL, Snowflake, dbt, dbt Core, dbt Cloud, Python
• Google Analytics (GA4), Hightouch

Benefits

• Opportunity to travel on one of our tours every year at no cost without using paid vacation.
• Four weeks of paid vacation and twelve paid holidays.
• Professional growth including monthly trainings, workshops, and talks with global leaders and experts.
• 25% company match on your 401(k).
• Medical, dental, and vision coverage, plus life and disability insurance.
• Paid international business travel.
• Wellness benefits and annual fitness reimbursement.
• EF program discounts, including travel, language schools, childcare, and more.
• Flexible spending accounts including dependent-care, healthcare, and commuter.
• Discounts at local venues and businesses.

Bonus focus areas

• Reverse ETL/data activation (Hightouch) and product data that powers marketing and advertising.
• Semantic layers or metric layers used by LLM-driven analytics or self-serve tools.
• Digital analytics event data, especially GA4: sessions, events, and models connecting web behavior to business outcomes.
• Measurement for experimentation, lifecycle, or marketing attribution.