Skip to content
AnthropicAnthropic

Analytics Data Engineer

Builds and manages data pipelines using dbt, SQL, and Python to create scalable analytics infrastructure. Develops dashboards and self-serve tools for company-wide metrics, partnering with Engineering, Product, and GTM teams. Requires 5+ years experience.

About the job

Responsibilities

  • Understand the data needs of stakeholder teams in terms of key data models and reporting, and translate that into technical requirements
  • Define, build and manage key data pipelines in dbt that transform raw logs into canonical datasets
  • Establish high data integrity standards and SLAs to ensure timely, accurate delivery of data
  • Develop insightful and reliable dashboards to track performance of core metrics that will deliver insights to the whole company
  • Build foundational data products, dashboards and tools to enable self-serve analytics to scale across the company
  • Influence the future roadmap of Product and GTM teams from a data systems perspective
  • Become an expert in our organization’s data models and the company's data architecture

Requirements

  • 5+ years of experience as an Analytics Data Engineer or similar Data Science & Analytics roles, preferably partnering with GTM and Product leads to build and report on key company-wide metrics
  • Expertise in building multi-step ETL jobs, building robust data models through tooling like dbt; proficiency with workflow management platforms like Airflow and version control management tools through GitHub
  • Expertise in SQL and Python to transform data into accurate, clean data models
  • Experience building data reporting and dashboarding in visualization tools like Hex to serve multiple cross-functional teams
  • A bias for action and urgency, not letting perfect be the enemy of the effective
  • A “full-stack mindset”, not hesitating to do what it takes to solve a problem end-to-end
  • Experience building an Analytics Data Engineering (or similar) function at start-ups
  • A strong disposition to thrive in ambiguity, taking initiative to create clarity and forward progress

Compensation

Annual Salary: $275,000—$370,000 USD

Skills

dbt, SQL, Python, Airflow, GitHub, ETL, Hex, Data Pipelines, Data Models, Dashboards

Fluidstack

Fluidstack

Austin, TX
Data Engineer
$269k+/yrOn-site5+ YOEData Engineering

Build and own production data pipelines, knowledge graph data models, and structured datasets from messy sources (PDFs, spreadsheets, telemetry) to power internal tools, dashboards, and ML models at a frontier AI compute infrastructure company. Requires experience operating depended-on pipelines, schema modeling, data quality engineering, and unstructured data extraction.

Thinking Machines Lab

Thinking Machines Lab

San Francisco, CA

Data Operations
$250k+/yrHybridData Engineering

Own end-to-end data sourcing and vendor operations that help researchers train and evaluate frontier AI models. The role requires strong judgment, communication, problem-solving, and comfort managing ambiguous, fast-changing projects.

Anthropic

Anthropic

San Francisco, CA
Data Engineer, GTM
$320k+/yrHybrid5+ YOEData Engineering

Build and govern quote-to-cash data models and products integrating Salesforce, CPQ, billing, and finance systems. The role requires 5+ years of data engineering experience, strong SQL and Python skills, and expertise in self-service analytics for GTM teams.

OpenAI

OpenAI

Mountain View, CA
Data Engineer, Monetization Data Platform
$230k+/yrOn-siteData Engineering

Build and operate scalable monetization data platforms, pipelines, models, and quality systems spanning product, financial, and operational data. The role partners with Product Engineering, Finance, Accounting, Analytics, and GTM teams to deliver reliable, observable data products.

The Voleon Group

The Voleon Group

New York, NY
Software Engineer, Strategy Research Analytics
$230k+/yrRemote3+ YOEData Engineering

Build and evolve reliable analytics infrastructure, pipelines, schemas, and foundational datasets supporting quantitative research across strategies. The role requires strong Python and SQL skills, distributed data-platform experience, and ownership of observability, performance, and reproducibility.