Analytics Engineer
Analytics Engineers at AI companies sit between data engineering and analytics, building and maintaining the data models, metrics layers, and self-serve analytics that the rest of the company relies on to make decisions. The day-to-day is SQL- and dbt-heavy: designing dimensional schemas and warehouse models, defining metric logic that holds across teams, building documentation and tests, and partnering with finance, product, and GTM stakeholders on what the numbers should mean. Where the role differs from data engineering is in proximity to business questions—Analytics Engineers spend more time defining metrics and enabling self-service than building ingestion pipelines, even when the technical surface looks similar. Specific data domains range from product usage and revenue (most companies) to compute and infrastructure economics (at AI infrastructure companies), but the underlying methodology is the same.
Skills
What companies are looking for in this role.
Designing and building scalable data models and transformation pipelines to convert raw data into clean, reliable analytical assets
Partnering with cross-functional stakeholders to translate business requirements into technical data architecture and analytical solutions
Defining and implementing semantic layers that standardize business metrics and enable consistent metric definitions across the organization
Creating and maintaining dashboards and self-service analytics tools that empower non-technical stakeholders to answer their own questions
Establishing data quality standards, testing frameworks, and monitoring systems to ensure data reliability and accuracy
Designing data governance frameworks and documentation standards to ensure data accessibility and maintainability at scale
Owning the full analytics stack from data modeling through semantic layers to dashboards and self-service tooling
Conducting deep-dive analyses to surface insights about business performance, cost drivers, and operational patterns
Ingesting and cleaning data from multiple disparate sources including billing systems, CRM platforms, and cloud providers
Building financial data foundations that power revenue metrics, unit economics, and board-level reporting
Collaborating with data engineering teams to define data contracts, SLAs, and pipeline orchestration standards
Building data products that integrate event data, product usage data, and financial data into unified analytical views
Leveraging AI and machine learning tools to automate data insights, anomaly detection, and self-serve analytics capabilities
Designing and implementing metrics and measurement systems for specialized domains such as search quality, infrastructure efficiency, and platform economics
Building data infrastructure for supply chain and procurement analytics, integrating cost, capacity, and vendor data
Operating with high autonomy and ownership while driving ambiguous, high-impact business problems to completion
Communicating complex data insights clearly and persuasively through presentations, memos, and visualizations
Building trusted relationships with business and technical stakeholders to understand decision-making needs
Setting technical direction, tooling choices, and quality standards as a founding or early analytics hire
Mentoring and supporting analysts of varying levels of experience and scope
Technology
The tools and technologies that define this role.
Open Jobs
28 open Analytics Engineer jobs across 21 companies.
Other Data & Analytics roles
Applies statistical modeling, machine learning, and experimentation to extract insights from data.
Builds and maintains data pipelines, warehouses, and infrastructure that enable analytics and ML.
Analyzes data to generate actionable business insights, builds dashboards and reports.
Data professionals specializing in marketing and go-to-market measurement, attribution modeling, and revenue intelligence. Focuses on building analytical frameworks, experimentation, and data-driven insights to optimize GTM strategy. The emphasis is on analytics methodology and data infrastructure for marketing.
Manages data labeling, annotation, and curation operations for machine learning.