Analytics Engineer

Analytics Engineer / Senior Analytics Engineer at California (USA) Overview Join one of the world’s largest digital media organizations as an Analytics Engineer supporting data platforms for News web, mobile, and internal products. This role sits at the intersection of analytics and engineering. You’ll work within an established Analytics Engineering team responsible for building and maintaining the datasets, data models, and production pipelines that power reporting, analytics, and downstream applications. We’re looking for an experienced engineer who can quickly become productive within an existing environment, contribute to ongoing development, support production operations, and help ensure the reliability of our analytics platform. Our team uses a modern analytics engineering stack centered around dbt, Databricks, AWS, and GitHub, with a strong emphasis on engineering best practices, collaboration, and high-quality production systems. Must-Have Qualifications • 5+ years of experience as an Analytics Engineer, Data Engineer, or Data Analyst with a strong engineering focus • Expert-level SQL skills, including complex query development, optimization, and data modeling • Extensive hands-on experience developing and maintaining production dbt projects • Experience working with cloud-based data platforms (AWS preferred) • Experience with modern data platforms such as Databricks, Unity Catalog, Snowflake, or similar technologies • Experience supporting production data pipelines and troubleshooting operational issues • Ability to independently investigate unfamiliar systems and drive issues to resolution with minimal supervision • Strong communication and collaboration skills when working across technical teams Nice-to-Have Qualifications • Experience with Databricks • Experience with PySpark or other distributed data processing frameworks • Experience with CI/CD workflows for analytics engineering (GitHub Actions or similar) • Familiarity with dimensional modeling and medallion architecture • Experience supporting business-critical reporting environments Day-to-Day Responsibilities Data Modeling & Transformation • Build, enhance, and maintain production data models using dbt • Extend existing dimensional models while following established engineering patterns • Develop transformations across structured and semi-structured datasets • Apply medallion architecture principles across Silver and Gold data layers • Optimize models for scalability, maintainability, and performance Production Pipeline Support • Contribute to the operation and ongoing enhancement of production data pipelines running on Databricks • Monitor scheduled workflows and investigate pipeline failures during business hours • Troubleshoot production issues across data models, orchestration, and upstream dependencies • Coordinate with upstream and downstream teams to resolve data delivery issues • Restore production workflows and communicate status during operational incidents • Escalate larger architectural or platform issues when appropriate SQL & Data Engineering • Write and optimize complex SQL across large-scale datasets • Develop and maintain dbt models that support reporting and downstream systems • Contribute to PySpark-based data processing where appropriate • Improve query performance and warehouse efficiency Problem Solving & Analysis • Investigate data quality issues and implement appropriate fixes • Perform exploratory analysis to support engineering and business needs • Translate loosely defined requirements into practical data solutions • Independently diagnose issues across unfamiliar datasets and existing codebases Collaboration • Work closely with analytics engineers, data engineers, product teams, and business stakeholders • Participate in code reviews and team design discussions • Collaborate with upstream and downstream engineering teams when dependencies or production issues arise • Communicate progress, risks, and blockers clearly throughout assigned work Quality & Standards • Follow established engineering standards for development, testing, deployment, and code review • Implement testing and validation for new and modified dbt models • Update documentation as needed to reflect implemented changes • Produce maintainable, production-ready code that aligns with existing team practices How We Work Ownership & Execution • Work is prioritized through a structured backlog and assigned based on team priorities and capacity • Team members take ownership of individual features, operational issues, and production incidents while collaborating closely across the broader engineering team • Engineers contribute across the full development lifecycle—from implementation through deployment, production support, and maintenance Engineering Standards • Development follows established patterns for modeling, testing, deployment, and code review • CI/CD workflows are used to promote consistency and code quality across environments • Development and production environments are clearly separated within our Databricks platform • Engineers are expected to work within existing architecture and engineering standards while delivering high-quality solutions AI-Assisted Development • We incorporate AI tools such as GitHub Copilot into our engineering workflow to improve productivity and accelerate development • AI is used within established engineering standards and review processes • Candidates are not expected to be AI experts but should be comfortable using modern developer tooling as part of day-to-day engineering work What We’re Looking For • Experienced analytics engineer who can become productive quickly within an existing codebase • Someone who enjoys solving complex data problems with an engineering mindset • A self-directed engineer who is comfortable investigating production issues and driving them to resolution • A pragmatic developer who values maintainable, reliable, and well-tested solutions • A collaborative teammate who communicates clearly and works effectively across engineering and business partners • Someone who can confidently contribute to an established analytics platform while maintaining high engineering standards