Build and maintain curated analytical datasets, data models, and metric definitions that serve as trusted sources for dashboards and self-serve analytics. Partner with data science and product teams to translate business questions into reliable, well-modeled data assets while enforcing quality standards.
180k – 200k/yr
On-site3+ YOEData Engineering
About the role
What you will be doing
Design, build, and maintain curated analytical datasets and data models that serve as canonical sources for metrics, dashboards, and analyses
Own metric definitions end to end, from partnering with data science and product team to define what we should measure, to implementing it in production, to surfacing it in visualization tools
Build and maintain executive-level dashboards and self-serve reporting tools that enable business stakeholders to answer their own questions
Partner closely with data science, product manager, and engineering teams to translate business questions into well modeled, performant, and discoverable data assets
Establish and enforce data quality standards through testing frameworks, documentation, and monitoring for the datasets you own
Drive adoption of consistent data modeling patterns, naming conventions, and documentation norms across the data organization
What you should have
3+ years of experience in analytics or data engineering with a strong focus on building curated, consumer-facing datasets
3+ years of experience in designing, developing, and maintaining robust data models from structured and unstructured sources
3+ years of experience writing accurate and effective code in SQL
Experience with designing data model to power a variety of use cases, including experimentation
Fluency in Python or another programming language, with solid fundamentals in version control and CI/CD practices
Experience building and owning executive-level dashboards and reports using BI tools (e.g., Looker, Tableau, or similar)
Strong business acumen, you will partner with data scientists and product managers to translate ambiguous business questions into concrete metric definitions and data models
Excellent communication, comfortable being the connective tissue between technical and business teams
Desire to work with amazing, passionate people who care deeply about solving challenging problems to improve Discord
Bonus Points
Passion for Discord or online communities
Experience building or contributing to a semantic layer or metrics store
Experience with modern analytics and data engineering tools and workflows (dbt, BigQuery, or similar)
Experience working on monetization and/or subscription products
Build privacy-first data infrastructure and analytics platform for a secure mobile carrier. Design warehouses, pipelines, and self-serve dashboards with privacy baked in from day one. Requires 4+ years data engineering experience, strong SQL, Python/Go, and cloud warehouse skills.
180k – 235k/yrHybrid4+ YOEData Engineering
Data Engineer
Actively AINew York, NY
Own data and analytics end-to-end: architect internal systems, build metrics/dashboards, and translate customer and product signals into structured inputs for AI agents.
180k – 210k/yrOn-siteData Engineering
Data Engineer
BasetenSan Francisco, CA +1
Builds and scales internal data platform by designing data models, pipelines, and analytics infrastructure to transform raw product/business data into reliable datasets for company-wide decision-making. Partners with stakeholders across Product, Engineering, Finance, Marketing, and Sales.
180k – 250k/yrHybridData Engineering
Software Engineer - Data Platform
xAIPalo Alto, CA +1
Builds and operates petabyte-scale data platform infrastructure using Kafka, Spark, Flink, and Trino to power real-time ML pipelines and analytics. Requires expertise in distributed systems, stream processing, and systems languages like Rust, Go, or Scala.
180k – 440k/yrHybridData Engineering
Software Engineer, Distributed Data Systems
ExaSan Francisco, CA
Architects and builds massive-scale data infrastructure for web crawling, embedding model training, and real-time search, handling hundreds of petabytes. Requires expertise in lakehouse architectures, distributed processing pipelines, and streaming systems like Kafka and Flink.