PMC: Principal Analytic Engineer

PMC is seeking a Senior Analytic Engineer to help shape how data is sourced, integrated, and structured within our data warehouse, ensuring it is consistent, trusted, and usable for decision-making across PMC. This role sits at the intersection of analytics engineering and data engineering, with responsibility for helping build and maintain the data models, transformation workflows, and quality standards that make data from CMS platforms, web analytics tools, ad tech systems, and internal applications reliable and useful.

As a senior individual contributor, you will be a hands-on builder who designs scalable data models, contributes to ingestion and transformation strategies, and partners with Data Engineering and analytics stakeholders to deliver well-structured, business-ready datasets. You will have a chance to work on some of the best brands in media including Variety, Billboard, Deadline, WWD, and Rolling Stone.

Key Responsibilities:

Design, build, and maintain scalable data models that transform raw data into structured, business-ready datasets

Develop and improve transformation workflows (e.g., dbt) within Snowflake and BigQuery environments, contributing hands-on to complex modeling efforts

Help define how data is sourced and integrated from key systems, including CMS platforms, web analytics tools, ad tech platforms, and business applications

Support the identification and maintenance of appropriate sources of truth across systems, with attention to data accuracy and usability

Establish and follow standards for how data is organized, documented, and used across the data warehouse

Implement data quality checks, validation frameworks, and monitoring to ensure reliability of critical datasets

Partner closely with Data Engineering on ingestion patterns, pipeline performance, and upstream data quality

Build datasets that support consistent analysis across Editorial, Product, Audience, Ad Sales, and Finance use cases

Support BI tools such as Looker with well-defined, governed datasets that reduce fragmentation in reporting

Translate analytics and reporting requirements into scalable data models and transformations

Investigate data discrepancies, document business logic clearly, and improve consistency in metric definitions

Contribute to best practices for modeling, transformation, and documentation across the team

As part of a team, support break/fix scenarios when necessary and serve in an on-call rotation

Qualifications:

You do not need to check every box for the experience below. If you are passionate about this opportunity, we would love to hear from you.

6+ years of experience in analytics engineering, data engineering, or related roles

Strong SQL skills and experience designing scalable data models for analytics and reporting use cases

Experience working with modern data warehouses such as Snowflake and/or BigQuery

Hands-on experience building transformation workflows using dbt or similar tools

Experience working with complex, multi-source data environments, including web analytics, ad tech, and business systems

Familiarity with Google Analytics 4 and Parse.ly analytics platforms

Experience working with advertising data, including Google Ad Manager or related programmatic ecosystems

Experience investigating and reconciling conflicting data sources

Experience implementing data quality, validation, and monitoring practices

Experience supporting BI tools such as Looker

Familiarity with cloud environments such as AWS and/or GCP

What Success Looks Like:

Trusted, well-documented datasets for audience, content, and revenue reporting are broadly used across teams

Recurring discrepancies between key systems are reduced through thoughtful data modeling, validation, and source selection

Teams increasingly rely on shared, well-structured datasets instead of one-off logic and fragmented reporting

Standardized data models are adopted across core reporting use cases, reducing duplicate logic and inconsistency

Data consumers have greater confidence in key metrics and can more easily understand how important KPIs are defined

A more unified and usable data layer supports better decisions around content performance, audience engagement, and monetization

Improvements to data models, documentation, and metric definitions are durable, maintainable, and shared across the Data team

As PMC values in-person collaboration and team cohesion, employees work onsite 4 days a week and 1 day remotely with a focus on maintaining a vibrant and inclusive culture.

Typical wage range: $150k - $170k. Factors that could be used to determine your actual salary may include your specific skills, years of experience and comparison to current employees already in this role. If you have more or less experience than specified on this job posting, please apply and list your salary expectations.

If hired, employee will be in an “at-will position” and the Company reserves the right to modify base salary (as …