Senior Data Engineer

Ground News

16d•Remote

About The Position

Ground News is a platform that makes it easy to compare news sources, read between the lines of media bias, and break free from algorithms. In a world where political polarization and media distrust are at an all-time high, Ground News offers people a better way to stay informed and see every side of the story. Our mobile app, web app, and browser extension are home to a community of mindful readers who use our news comparison platform to engage with news beyond their filter bubble. Unlike most news companies, our values do not align with any specific political ideology. Unlike most tech platforms, we don’t rely on algorithms to keep users on our platform to sell more ads. We are supported by our readers who pay for a subscription to build a more nuanced understanding of the news, the world, and themselves. As a senior Data Engineer, you'll build the analytical foundation that Ground News runs on. You'll sit within our ML chapter and work out of the Platform & Infrastructure pod, splitting your time between developing the integrations that bring data into our warehouse and analytical layers that serve KPIs for our Growth & Monetization, Core Experience, and Content Intelligence pods. This is a senior, hands-on role for someone who thinks in reusable models across the org. You'll design scalable, conformed data models that let analysts answer their own questions without waiting on you, and you'll own the reliability of those models end to end: ingestion, data quality, accuracy, coverage gaps, and failures both upstream and downstream. When a number looks wrong, you're the person who can trace it from the event that produced it to the dashboard that reported it. You'll also be a key liaison between the people who ask the questions and the systems that answer them, translating what analysts, customer success, and product stakeholders need into data they can trust and reuse.

Requirements

5+ years of experience building and operating production data platforms and analytical models.
Deep expertise in SQL and Python.
Broad experience with dimensional modelling: grain, table types, conformed dimensions, and load patterns.
Hands-on experience with modern data tooling: BigQuery, dbt, Dagster, and ingestion tools such as Fivetran, Airbyte, or Google Datastream.
Experience with data stores and event-driven systems such as PostgreSQL, Elasticsearch/OpenSearch, and message queues or streams (Kafka, SQS, or Pub/Sub).
SRE-oriented experience: monitoring, alerting, incident response, and performance optimization for data integrations.
Experience shipping data and metrics safely, with the validation habits to keep wrong numbers off dashboards and an eye on the compute cost behind them.

Responsibilities

Design and build scalable, conformed data models and analytical layers that serve experimentation and company KPIs.
Own the connectors that land data in the warehouse end to end, from replicated backend tables to custom connectors you build yourself and third-party managed sources.
Manage infrastructure as code and deployment workflows across AWS and GCP, using Kubernetes and OpenTofu (Terraform) to ship changes safely.
Own the pipelines you build, monitoring freshness and responding to failures with urgency.
Act as a senior voice in data architecture decisions, from selecting table types and datastores to designing the schemas from backend to semantic layers.
Partner directly with analysts, ML engineers, and product stakeholders to turn business questions into data models they can self-serve from.
Champion high engineering quality through testing, code reviews, observability, and thoughtful operational practices.