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 warehouse models and dimensional schemas
Partnering with cross-functional stakeholders to translate business needs into data solutions
Writing expert-level SQL including window functions, complex aggregations, and query optimization
Transforming raw data into production-grade, well-documented data models
Building and maintaining data pipelines with testing, monitoring, and data quality validation
Defining, implementing, and maintaining consistent metric definitions across the organization
Creating dashboards and enabling self-serve analytics for non-technical stakeholders
Establishing data governance, documentation standards, and best practices
Implementing financial data models and ensuring accurate revenue, ARR, churn, and unit economics reporting
Designing event instrumentation schemas and product telemetry strategies
Reconciling data across multiple systems to ensure single source of truth
Modeling usage-based and subscription billing data for financial accuracy
Implementing slowly changing dimensions and managing temporal data patterns
Building attribution models and marketing analytics infrastructure
Building AI-powered data agents and semantic layers for machine-readable data
Automating data workflows and implementing self-healing, autonomous data quality systems
Integrating AI tooling into analytics workflows for acceleration and automation
Designing product analytics infrastructure and data consumption patterns
Communicating clearly and proactively with both technical and non-technical teams
Collaborating with product, engineering, and business teams to align on definitions and priorities
Translating complex technical concepts for business and executive audiences
Writing clear, maintainable documentation that reduces ambiguity and follow-up questions
Taking ownership and moving quickly to iterate on data solutions
Managing stakeholder expectations and driving consensus on complex data decisions
Technology
The tools and technologies that define this role.
Open Jobs
29 open Analytics Engineer jobs across 21 companies.
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