Principal Data Engineer

This is a Full Time Job

We are seeking a Principal Analytic Engineer to lead 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 data engineering and business operations, with responsibility for shaping how data from a wide range of systems–CMS platforms, web analytics tools, ad tech systems, and internal applications–is collected, standardized, and made available for use.

As a senior individual contributor, you will operate as both architect and hands-on builder: designing data models, guiding data ingestion and transformation strategies, and working closely with stakeholders to ensure data aligns with real business needs across editorial, audience, product, and advertising teams.

Key Responsibilities
• Define how data is sourced and integrated from key systems, including CMS platforms, web analytics tools, ad tech platforms, and business applications
• Evaluate and establish appropriate sources of truth across competing systems, ensuring data is accurate and fit for use
• Design and maintain scalable data models that transform raw data into structured, business-ready datasets
• Lead development of transformation workflows (e.g., dbt) within Snowflake and BigQuery environments, contributing hands-on to complex modeling efforts
• Establish 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 with Data Engineering on ingestion patterns, pipeline performance, and upstream data quality
• Structure data to support consistent interpretation across Editorial, Product, Audience, Ad Sales, and Finance teams
• Support BI tools (e.g., Looker) with well-defined, governed datasets and reduce fragmentation in reporting
• Translate business needs into scalable data solutions, working closely with cross-functional stakeholders
• Act as a central authority on data meaning, resolving questions around definitions, discrepancies, and interpretation
• Establish best practices for modeling, transformation, and documentation while mentoring analysts and engineers
• 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.
• 8 years of experience in data engineering, analytics engineering, or related roles
• Expert-level SQL and strong experience designing scalable data models for analytics and reporting use cases
• Proven experience working with modern data warehouses (e.g., Snowflake, BigQuery) and cloud environments (AWS and/or GCP)
• Hands-on experience building transformation workflows (e.g., dbt or similar tools)
• Experience working with complex, multi-source data environments (e.g., combining web analytics, ad tech, and business systems)
• Familiarity with Google Analytics 4 and Parsely analytics platforms
• Experience working with advertising data (e.g., Google Ad Manager, programmatic ecosystems)
• Experience evaluating and reconciling conflicting data sources
• Experience implementing data quality, validation, and monitoring practices
• Experience supporting BI tools such as Looker

What Success Looks Like
• Clearly defined and trusted sources of truth for audience, content, and revenue data across all teams
• Elimination of recurring discrepancies between key systems (e.g., web analytics vs. ad platforms) through thoughtful data modeling and source selection, and ability to confidently answer the question “why are these two numbers different?”
• Increased business partner engagement in self-serve analytics platforms, with teams relying on shared, well-structured datasets instead of one-off analyses
• Broad adoption of standardized data models, minimizing fragmented reporting and duplicate logic
• Strong alignment across Editorial, Product, and Ad Sales on how performance is measured and interpreted
• Increased confidence in data, with stakeholders relying on outputs without needing to independently validate or reconcile them
• A unified, usable view of data that enables better decisions around content performance, audience engagement, and monetization by consolidating overlapping data models and taking ownership of term definitions across all data models to bring standardization and clarity to reporting semantics across the company
• This role succeeds through strong collaboration with other members of the Data team–sharing context, reviewing work, and co-owning decisions–so that improvements to data models and metrics are durable beyond any single individual.

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.