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Lead Data Engineer
2 months ago
We are a pioneering B2B Software as a Service (SaaS) company that is revolutionizing the auto collision industry with cutting-edge Artificial Intelligence (AI) and Machine Learning (ML) solutions. Our mission is to reduce the number of collisions and create a safer transportation future.
Our flagship product, Revv ADAS, is an AI platform that automates the complex research needed to understand Advanced Driving Assistance Systems (ADAS) for auto repair businesses. The ADAS market is growing rapidly, expected to rise from $64 billion to over $212 billion by 2034, driven by software innovations.
Backed by a Series A investment led by Left Lane Capital and others, we have raised over $13 million in our first year, positioning ourselves for rapid growth. We are at the forefront of delivering cutting-edge software that delights our customers.
About the RoleAs the Lead Analytics Engineer, you will play a pivotal role in defining the Analytics function at Reve Labs, Inc. and be responsible for establishing a scalable data warehouse, modeling data for consumption by various stakeholders in the business, and defining best practices with our data for years to come.
The ideal candidate will be opinionated and driven to apply their expertise to create a best-in-class Analytics organization in a fast-growing environment, using both tools from the modern data stack (dbt, Fivetran/Airbyte, Metabase/Looker, etc) as well as Python and shell scripts with a focus towards getting the job done.
Key Responsibilities- Establish a Data Warehouse: Work with our engineering team to build a data warehouse from the ground up, providing recommendations and hands-on expertise for warehouse selection and CI/CD. Use low- and no-code ELT tools like Fivetran to pull data from our various sources (Stripe, Salesforce, Postgres) into the warehouse.
- Collaborative Data Modeling: Craft intuitive and user-friendly data models in dbt to meet stakeholder requirements for finance, product, and sales teams to draw insights from our data using SQL and data exploration tools like Metabase.
- Foster a Self-Serve Analytics Culture: Set up analytics tooling and documentation that enables self-service for stakeholders across the business.
- Expertise with SQL & dbt: A must-have for this role.
- Proficiency with Python or equivalent: A strong understanding of programming concepts is required.
- 5+ years of experience in Analytics Engineering, Data Engineering, or Data Analytics roles: A proven track record of success in a similar role is essential.
- Skilled in dimensional and reporting data models: Dimension & fact tables, time series aggregations, etc.
- High attention to detail and a compulsion for data governance, documentation, and best practices: A strong focus on quality and process is required.
- Excellent communication skills: The ability to translate complex stakeholder reporting requirements into scalable data models.
- A desire to be hands-on with all levels of the analytics stack: From dbt CI/CD to dashboards & visualization tools.
- Hybrid work environment: For HQ employees, a mix of in-office and remote work.
- Option to be fully remote: For those who prefer to work from home.
- Multiple health, dental, and vision plan options: A range of benefits to suit different needs.
- 401k plan: A retirement savings plan.
- Unlimited PTO: Time off when needed.
- Complimentary Headspace account: A mindfulness and meditation tool.