Staff Analytics Engineer

Role overview A senior Analytics Engineering role focused on architecting scalable, high-quality data pipelines and data models that power business-critical analytics, AI, and machine learning initiatives. The position blends deep technical leadership with cross-functional partnership, setting enterprise-wide modeling strategies and elevating data governance, discoverability, and accessibility across the organization. The work centers on turning raw data into trusted, self-serve products that drive measurable business outcomes.

Responsibilities - Architect scalable data models and high-quality ELT pipelines that form the backbone of a core data lake, applying modern orchestration and transformation tools. - Design and launch self-serve analytics products spanning data discovery, consumption, and enablement, going beyond standard dashboarding to surface root causes tied directly to business results. - Act as a technical leader for major initiatives, shaping architectural direction and mentoring others to build a culture of continuous learning. - Serve as a data leader for the business, raising data literacy, eliminating pain points, and closing critical data gaps. - Partner with data scientists, product engineers, and business stakeholders to define, curate, and govern high-fidelity data, mapping KPI interrelationships that maximize ROI. - Develop new tools and frameworks collaboratively, including AI-driven capabilities that make data more accessible and easier to consume.

Requirements - 10+ years of experience in data or analytics engineering with deep expertise in data architecture, pipelines, and reporting. - Expert-level command of relational databases, DRY data modeling, and efficient SQL authoring. - Hands-on expertise with modern data stack components such as Databricks, Delta Lake, Airflow, dbt, Redshift, or DataHub; dbt is required and Databricks preferred. - Proven experience crafting self-serve reporting solutions using BI tools like Looker or Sigma. - Strong root-cause analysis skills, ideally paired with a background in data science or business. - Experience implementing enterprise-level data observability frameworks such as Monte Carlo or Great Expectations.

Nice to have - Familiarity with AI-assisted development tools like Claude, Gemini, or Cursor for streamlining data processing. - Track record leading cross-functional RFCs and driving technical standards across multiple engineering teams. - Experience with data lake architecture, including batch and streaming patterns. - Background building feature stores or pipelines for LLM fine-tuning and RAG architectures. - Demonstrated mentorship that elevates data culture in a remote environment.

Benefits and work setup - Zone-based compensation structure aligned to location and experience, with published US pay ranges by zone. - Comprehensive health and wellness benefits, performance bonus, and RSU equity programs. - Global perks designed to support growth wherever the employee is based.