Senior Analytics Engineer

Zillow

1d•$144,100 - $242,300•Remote

About The Position

As an Analytics Engineer, you are responsible for building the trusted, governed, and scalable data models and metrics that underpin how the company measures its business and makes decisions. You translate raw data into well-structured analytical assets that serve dashboards, self-service tools, data science workflows, and increasingly, AI-powered analytics interfaces.

Requirements

Bachelor’s degree in Data Science, Computer Science, Information Systems, Statistics, Business, or a related field.
5+ years of experience in analytics engineering, data engineering, business intelligence, or a closely related field, working with large datasets to develop reliable analytical data models and data products.
Demonstrated experience owning analytical data products across multiple data domains end-to-end and independently solving complex data modeling and metric definition challenges.
Proven ability to lead cross-functional workstreams, translate business needs into technical specifications, prioritize competing requests, and manage multiple projects and upstream dependencies in alignment with team and business goals.
Deep proficiency in SQL, data transformation frameworks such as dbt or equivalent, and analytical modeling approaches, including dimensional, denormalized, and entity-relationship models, with sound judgment about granularity, aggregation, reuse, and business logic placement.
Experience working in online data lakehouses like Databricks or Snowflake and proficiency in Git
Strong experience applying software engineering and operational practices to analytics workflows, including version control, CI/CD, code review, automated testing, data quality checks, monitoring, and root-cause analysis, with the ability to improve team standards and reliability.
Experience designing and maintaining semantic layer components, metrics-as-code, and metadata for multiple consumers, with an understanding of data governance and how BI tools, data science workflows, and AI-powered interfaces interpret metric definitions.
Ability to communicate technical trade-offs clearly, align stakeholders on shared metric definitions, write concise self-service documentation, and mentor teammates on modeling and engineering best practices.
Experience using AI-assisted coding tools with the judgment to validate generated SQL, transformation logic, and documentation, prevent duplicated or inconsistent business logic, and independently explain and defend your design decisions.

Responsibilities

Lead the design, development, and maintenance of trusted, scalable analytical data products, including reusable data models, standardized metrics, and semantic layer components that support business intelligence, self-service analytics, data science, and AI-powered interfaces.
Translate complex business requirements into well-structured data models and precise metric definitions, ensuring business logic is implemented in the appropriate layer and applied consistently across teams and analytical surfaces.
Partner with Data Engineering, Data Science, Business Intelligence, Product Management, and business stakeholders to prioritize analytics engineering initiatives, define technical requirements and data contracts, and manage cross-team dependencies.
Drive improvements in data quality, governance, discoverability, and documentation through automated validation, clear metadata, consistent metric definitions, and proactive resolution of data issues.
Build, optimize, and operate reliable last-mile data transformation pipelines using version control, code review, CI/CD, automated testing, monitoring, and alerting to reduce manual effort and improve maintainability.
Mentor and coach teammates on data modeling and engineering best practices, contribute to analytics engineering standards and roadmap decisions, and enable stakeholders to confidently use governed data products.