Senior Data Engineer, Analytics at Scribe

San Francisco, California, United States

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Not SpecifiedCompensation

Senior (5 to 8 years)Experience Level

Full TimeJob Type

UnknownVisa

TechnologyIndustries

Requirements

4+ years building production data pipelines and analytics infrastructure in cloud-native environments
Deep expertise in SQL and data transformation at scale
Advanced production experience with dbt (2+ years), including writing macros, packages, and implementing best practices
Hands-on experience with Snowflake performance tuning and cost optimization
Production experience with orchestration tools (Dagster or Airflow) and understanding of DAG design, dependency management, and monitoring
Experience building executive-level dashboards and BI solutions in Sigma (or similar tools) and understanding of semantic layers and data model optimization for AI-powered analytics
Understanding of dimensional modeling and analytics architecture, including designing data marts, fact/dimension tables, and semantic layers
Ability to work autonomously and drive complex projects end-to-end
Ability to thrive in ambiguity and communicate clearly with business stakeholders

Responsibilities

Work with the Data Engineering team and report to Fardad Golshany, partnering closely with Engineering, Product, GTM, and Finance teams
Design and build scalable analytics data marts in Snowflake using dbt that enable data science and insights across multiple business domains
Maintain existing data pipelines and build new ETL and reverse ETL workflows to ensure reliable data movement across systems
Stand up and scale orchestration infrastructure using Dagster or Airflow, implementing monitoring and alerting via Metaplane
Build dynamic executive-facing dashboards in Sigma to reduce time to insight and enable AI-powered self-serve analytics
Establish data governance standards with automated testing, comprehensive documentation, and semantic definitions that reduce ad-hoc queries
Optimize dbt pipeline performance by implementing incremental models, improving materializations, and applying engineering best practices

Skills

Key technologies and capabilities for this role

SnowflakedbtDagsterAirflowMetaplaneSigmaETLreverse ETLdata pipelinesdata governanceorchestrationmonitoringincremental models

Questions & Answers

Common questions about this position

What is the location and work arrangement for this role?

The role is hybrid, expecting candidates to live in or near San Francisco and work from the office 3 days per week. The Data Engineering team is distributed between San Francisco and New York City, with remote possible in these hubs for highly experienced candidates who can operate independently.

What salary or compensation does this position offer?

This information is not specified in the job description.

What are the key skills and experience required for this role?

Candidates need 4+ years building production data pipelines with deep SQL expertise, advanced dbt experience (2+ years) including macros and Snowflake tuning, production experience with Dagster or Airflow, Sigma or similar BI tools for dashboards, and knowledge of dimensional modeling.

What is the team structure like for this position?

You will work with the Data Engineering team and report to Fardad Golshany, partnering closely with Engineering, Product, GTM, and Finance teams.

What makes a candidate a great fit for this Senior Data Engineer role?

A great fit has 4+ years of production data pipeline experience in cloud environments, advanced dbt and Snowflake expertise, hands-on work with Dagster/Airflow and Sigma BI tools, and strong dimensional modeling skills to build scalable analytics infrastructure.

Scribe

AI-powered platform for creating guides

About Scribe

ScribeHow provides a platform for creating how-to guides and training documents using artificial intelligence. The platform automatically generates step-by-step guides for any web or desktop process, which helps businesses save time and resources by eliminating the need for manual documentation. It also features automatic redaction of sensitive information from screenshots to ensure compliance with data protection regulations. ScribeHow operates on a freemium model, allowing users to access a free version with the option to upgrade for more features. This approach attracts a diverse range of customers, from small businesses to large corporations. The user-friendly interface has been recognized for simplifying the guide creation process, contributing to the growth of businesses that use it.

San Francisco, CaliforniaHeadquarters

2019Year Founded

$53.5MTotal Funding

SERIES_BCompany Stage

Enterprise Software, Cybersecurity, AI & Machine LearningIndustries

201-500Employees

Benefits

Health Insurance

401(k) Retirement Plan

Unlimited Paid Time Off

Parental Leave

Remote Work Options

Home Office Stipend

Risks

Competition from Google and Microsoft could overshadow Scribe's AI offerings.

Rapid revenue growth may lead to operational challenges if not managed properly.

The freemium model poses financial risks if conversion rates to paid plans are low.

Differentiation

Scribe uses AI to automate step-by-step guide creation, saving time and resources.

The platform's automatic redaction feature ensures compliance with data protection regulations.

Scribe's freemium model attracts a broad range of customers, from small to large businesses.

Upsides

Scribe's revenue grew 400% last year, indicating strong market demand.

The rise of remote work increases demand for Scribe's asynchronous documentation capabilities.

Scribe's integration with tools like Asana and Trello enhances its utility for project management.

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