About the role

About The Meridiem

The Meridiem is a fast-growing technology media platform delivering timely, high-quality journalism across AI, startups, venture capital, cybersecurity, and enterprise tech. Our platform — built on Next.js 16 and deployed globally on Cloudflare Workers — serves hundreds of thousands of technology professionals through morning briefings, in-depth evening analysis, and breaking coverage that shapes how the industry understands technology.

Role Overview

We are looking for an Analytics Engineer to bridge the gap between raw data infrastructure and actionable business intelligence at The Meridiem. You will build the data models, metrics layer, and self-serve analytics foundations that empower every team — editorial, product, growth, and leadership — to answer their own questions with trusted, well-documented data. This role is central to building a truly data-informed media organization.

Key Responsibilities

• Design and maintain a modular, well-documented dbt project that transforms raw data into clean, reliable analytical models for content performance, reader behavior, and business metrics.
• Define and implement a metrics layer (using dbt metrics, MetricFlow, or a semantic layer tool) that provides a single source of truth for KPIs across the organization.
• Build dimensional models that connect editorial content metadata from Strapi CMS with reader engagement data, subscription events, and revenue metrics.
• Create and maintain data marts tailored to specific stakeholder needs — editorial analytics, audience growth, newsletter performance, and advertising effectiveness.
• Establish data quality testing and monitoring using dbt tests, data contracts, and alerting to catch issues before they reach dashboards and reports.
• Develop self-serve analytics capabilities — well-structured data models, clear documentation, and guided exploration tools that reduce dependency on the data team for routine questions.
• Write and maintain comprehensive documentation for all data models including business definitions, source-to-target mappings, known limitations, and usage examples.
• Collaborate with data engineers to improve source data quality, define new tracking requirements, and optimize pipeline performance.
• Partner with data analysts to understand recurring analytical needs and codify them into reusable, tested data models rather than one-off queries.
• Implement data governance practices — access controls, PII handling, data retention policies, and compliance with privacy regulations.
• Optimize warehouse query performance and cost by designing efficient materializations, incremental models, and appropriate partitioning strategies.
• Facilitate data literacy across the organization through training sessions, office hours, and accessible documentation.

Requirements

• 4+ years of experience in analytics engineering, data engineering, or a senior analyst role with significant data modeling responsibilities.
• Expert SQL skills with deep experience in data modeling — star schemas, dimensional modeling, wide tables, and incremental processing patterns.
• Strong proficiency with dbt (dbt Core or dbt Cloud), including advanced features like macros, packages, incremental models, and snapshot tables.
• Experience designing and implementing a metrics layer or semantic layer for consistent KPI definitions across an organization.
• Solid understanding of data warehousing concepts and hands-on experience with at least one cloud warehouse (BigQuery, Snowflake, Redshift, or Databricks).
• Strong data quality engineering skills — writing tests, implementing data contracts, and building monitoring that catches issues proactively.
• Excellent documentation habits with the ability to write clear data dictionaries, model READMEs, and onboarding guides that non-technical team members can follow.
• Experience enabling self-serve analytics and reducing ad-hoc query burden through well-designed data products.
• Clear communication skills with the ability to work across technical and non-technical teams, translating business questions into data model requirements.

Nice-to-Have

• Experience in media, publishing, or content platform analytics — understanding content performance, audience metrics, and editorial KPIs.
• Familiarity with Strapi CMS data structures or headless CMS content models.
• Experience with BI tools (Metabase, Looker, or Tableau) and designing models that work well for dashboard consumption.
• Knowledge of newsletter and email analytics (Beehiiv, Mailchimp, or similar platform data).
• Understanding of web analytics data — pageviews, sessions, attribution, and the nuances of behavioral event data.
• Experience with Cloudflare analytics or edge computing metrics.
• Familiarity with Python for data quality scripting, automation, or advanced transformations.
• Contributions to the dbt community — packages, blog posts, or open-source models.

What We Offer

• Competitive salary ($120,000 - $155,000) with meaningful equity in a Series A startup.
• Fully remote work environment with flexible hours and async-first communication.
• Annual learning and development budget of $2,500 for conferences, courses, and certifications (dbt certification encouraged).
• Premium health, dental, and vision insurance coverage.
• Generous PTO policy with company-wide recharge weeks.
• The opportunity to build the analytics foundation of a media company from the ground up, directly shaping how editorial and business decisions are made.
• Modern data stack tooling and the autonomy to choose the right tools for the job.

How to Apply

Send your resume, a brief description of a data modeling project you are proud of, and a note on your approach to building self-serve analytics in a growing organization to support@themeridiem.com with the subject line "Analytics Engineer Application." We review applications on a rolling basis and aim to respond within one week.