[Hiring] Staff Data Engineer @Advance Local

Role Description

Advance Local is looking for a Staff Data Engineer to be the Analytics Platform Lead and own the analytics layer that connects our modern data platform to the business. This is an intentionally technical and execution-oriented role for someone who has built this before at scale. You will work directly with business unit leads, finance, editorial, and product to understand what questions matter, translate those into the right data models, write the code, build and validate the solutions, and make sure the business can act on what they surface. You will develop and mentor an existing senior analyst and partner closely with our data architect and data engineering team.

Advance Local operates across multiple revenue lines that share an audience but have not had a common view of what drives performance. You will own that common view.

The base salary range is $180,000 per year.

What you'll be doing:

• Own the analytics layer across the business, serving as the connective tissue between the data platform and every team making revenue decisions across programmatic, subscriptions, affiliate, and data sales.
• Design, build, and maintain scalable data models and analytical solutions with a strong focus on correctness, performance, reusability, and documentation.
• Build and maintain semantic data models that establish consistent business definitions and metrics across the organization.
• Scope and deliver a content-level revenue and audience performance system of record, working iteratively with finance, editorial, and business unit leads as requirements evolve.
• Write, review, and own production-quality SQL and dbt code used in data transformations and analytical workflows.
• Move the business from descriptive reporting to insight, building the analytical foundation that answers not just what happened but why.
• Partner with BI teams and business units to advance how analytics work gets done across the company and accelerate the transition to Snowflake as the primary platform for analytics delivery.
• Design and lead statistically rigorous A/B testing and experimentation frameworks that inform product and business decisions.
• Build self-service analytics capabilities that reduce ad hoc request dependency and enable business unit leads to answer their own questions.
• Establish data quality, monitoring, and validation practices that build trust in the data across the organization.
• Contribute to analytics roadmaps and prioritization, balancing near-term delivery with long-term architectural health.
• Develop and mentor a senior analyst, including sprint planning, career development, and technical guidance.
• Collaborate with data engineering and data science teams to ensure the analytics layer is reliable, scalable, and aligned with downstream modeling and activation needs.
• Maintain thorough documentation for analytics models, data definitions, reporting logic, and data lineage.
• Stay current on modern analytics engineering practices, AI and data tooling, and industry trends.

Qualifications

• Bachelor's degree in computer science, statistics, economics, information systems, or a related field.
• Minimum 7 years of experience in data analytics, analytics engineering, or a closely related technical discipline, with demonstrated hands-on delivery at scale.
• Deep proficiency in SQL and dbt for scalable, modular, well-documented data modeling.
• Experience with Snowflake or equivalent modern cloud data warehouse; Snowflake experience strongly preferred.
• Strong experience designing data models, dimensional, semantic, or analytical, that support scalable analytics and are built for reuse and trust.
• Familiarity with semantic modeling approaches and how a well-structured analytics layer enables downstream AI and machine learning use cases.
• Proven track record designing and evaluating A/B tests and experimentation frameworks that drove measurable business outcomes.
• Experience building analytics for audience, content, subscription, or revenue use cases in an audience-driven or subscription-based business.
• Strong ability to gather requirements from non-technical stakeholders and translate them into well-scoped, well-documented engineering work.
• Experience building self-service analytics tools or enabling business users to work independently with data.
• Familiarity with BI tools such as Power BI, Looker, or ThoughtSpot.
• Experience with version control and CI/CD practices in a data engineering context.
• Understanding of data quality frameworks, testing, and monitoring approaches.
• Excellent communication skills for working with both technical and non-technical stakeholders.
• Ability to operate independently and build in an environment where the systems you are creating do not fully exist yet.