Early education is one of the greatest determinants of childhood outcomes, is a must for working families, and has a lasting social and economic impact. Brightwheel’s vision is to enable high quality early education for every child — by giving teachers meaningfully more time with students each day, engaging parents in the development of their kids, and supporting the small businesses that make up the backbone of the $175 billion early education market. Brightwheel is the most loved technology brand in early education globally, trusted by thousands of educators and millions of families.

We are a fully remote team with employees across every time zone in the US. Our team is passionate, talented, and customer-focused. Our exceptional investor group includes Addition, Bessemer Venture Partners, Chan Zuckerberg Initiative, GGV Capital, Lowercase Capital, Emerson Collective, and Mark Cuban. 

We believe that everyone—from our employees to the students, teachers, and administrators we serve— should be given the opportunity to learn and thrive, whatever their background may be. We celebrate diversity in all forms because it allows our team and the communities we serve to reach their full potential and do their best work. From decision making, to how we operate, we ground ourselves in our Leadership Principles every day. 

We are in a transformative period and recognize the importance of data to inform our immediate and long-term ambitions. To enable our business partners and analysts, we are introducing analytics engineering to brightwheel. You will join a small but mighty pod of Analytics Engineers to build out a foundational data environment that can scale beyond ad-hoc analytics to automated reporting, advanced analytics, experimentation and eventually data science. We are looking for candidates who have seen what a high performing data organization looks like and can thrive in a start-up environment. Most importantly, candidates with a track-record of navigating through ambiguity will be most successful in this role. 

You would be a great fit for this role if you are passionate about data and possess deep SQL, scripting and data modeling skills. You enjoy data discovery based on business problems and want to build scalable data models to enable self-service analytics. You have experience juggling multiple projects with shifting priorities, while continuing to deliver value to the business, and are a curious, detail oriented, self-starter who wants to take full ownership of high impact projects with visibility throughout the organization. 

What You’ll Do

• You will be responsible for building and maintaining the data layer for our analytics stack, top to bottom. Part data engineer, part data analyst, and part the connective tissue in-between, your responsibilities will span from raw data, up through cleaned, organized and analyzable data. You’ll apply software engineering practices like version control, automated testing, documentation, code review and continuous integration to our data stack. In a particular week, you will find yourself:
• Writing SQL in dbt to build robust, extensible, and performant data models in a cloud-based data warehouse that will drive business intelligence for the company.
• Working alongside  business users and data analysts, designing and building data models  that unlock their data discovery and analyses.
• Working with engineers to understand existing data models and implementing new data structures to unlock business insights and data comprehension for all.
• Improving organizational capacity by owning data discovery and cataloging tools that create visibility into business processes, especially for non-technical users.
• Troubleshooting, improving and scaling existing data models and reports.

Required Qualifications, Skills, & Abilities

• 6+ years of work experience as an analytics engineer, data engineer or data analyst.
• 4+ years of advanced SQL scripting, within a version controlled repository, with a demonstrated ability to refactor highly complex SQL queries to optimize performance.
• 3+ years of coding in dbt or similar transformation tools.
• Excellent data discovery and analytical skills to manage complex business data requirements and data platforms without much guidance.
• Strong communication skills with both business and technical stakeholders.
• Track record of requirements gathering, solutioning and documenting data models.
• Strong understanding of data quality and testing methodologies.
• Experience with monitoring and/or troubleshooting ETL pipelines within Airflow / Python.
• Experience working with GTM teams (Marketing, Sales, Customer Success) and third party tools such as Salesforce, Hubspot or Rev.io.

Preferred Experience

• 3+ years experience with data warehouse cataloging with proven ability to deliver end-to-end visibility and maximize the value of data.
• 2+ years experience building data models with dbt in a cloud based data warehouse platform.
• Experience in data science, building both the foundation and models required to implement machine learning solutions.
• Experience with deploying DBT data models with CI/CD.
• Building data warehousing from large scale transactional data stores and 3rd party systems.
• Experience supporting product analytics via clickstream datasets or tools such as Mixpanel.
• Embedded analytics / BI reporting data models (e.g. looker / cubejs)
• Knowledge of database permissions management

$137,000 - $218,000 a year