Senior AI Analytics Engineer

About the role

This role combines data engineering, marketing analytics, and applied AI to build trusted infrastructure for business insights. You will own the marketing data foundation and enable AI-driven analytics for global marketing teams.

Data roles turn raw information into decisions. Analysts query databases and build dashboards. Data scientists build models that predict outcomes. Data engineers build the pipelines that move and store data. All three work closely with business teams and need a mix of statistics, coding, and communication. Nearly every modern company runs on data teams, from startups to banks. A strong portfolio of past analyses matters more than degrees in many hiring decisions.

Key facts

What you'll do

The marketing data ecosystem is documented thoroughly, and data projects are versioned in GitHub for transparency and collaboration.

Requirements

You bring 3 5 years of experience in analytics engineering, data engineering, business intelligence, or a closely adjacent technical role. You have experience working in cross-functional settings with both technical and non-technical stakeholders. You communicate fluently in English and write clear, concise documentation. You are proficient in Python and SQL for production-grade data work. You apply data warehousing concepts, ETL/ELT workflows, and orchestration tools such as dbt and Airflow. You use strong data modeling skills, including dimensional modeling and data layering. You have hands-on experience with semantic layer tools, with Looker or LookML strongly preferred and Tableau, PowerBI, or similar accepted. You have hands-on experience with LLMs and agentic workflows, designing agent context and building or testing AI-assisted pipelines. Prior experience in marketing analytics, including B2C/B2B funnels, attribution models, CRM, and growth measurement, is preferred. Proficiency in Python, SQL, and hands-on experience with LLMs and agentic workflows are mandatory for this role.

Practical notes

This role is based in Brazil with remote flexibility. You will follow hybrid work expectations and may be asked about location-specific logistics during hiring. Travel may be required within Brazil and internationally based on business needs. Typical interview steps

Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.

Good to know

Wellbeing is central to this role, supported by digital tools and structured data workflows. The team uses modern data stacks including cloud warehouses, dbt, Looker, and Python-based tooling. Analytical rigor, documentation, and semantic clarity help AI agents produce reliable results. Cross-functional collaboration shapes how data standards and responsible AI practices are defined. Continuous learning and feedback drive growth for both the role and the broader organization.

Questions to ask

Worth asking in any interview: how the team measures success, who the role works with daily, what the onboarding looks like, and what the company is trying to achieve this year. Asking what past hires did well is a strong final question. Keep the list short and pick the questions that matter most to you.

Career growth

Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.