Senior Data Engineer

The Team You’ll Work With

The Data Products pillar builds high profile products that are customer-facing and revenue-generating, and power other Carta products with data and intelligence. Our group is responsible for some of the most critical and foundational products and services in Carta’s portfolio such as Valuations, Financials Integration, Document Services, and includes a centralized internal Data Organization consisting of Data Engineering, Data Science, ML/E & Analytics Engineering.

This role will be in the Data Engineering team supporting our marketing partners. The Data Engineering team builds and scales our analytics infrastructure to support all Carta business units. Our scope spans infrastructure, data integration, quality, visualization, and governance. Our team enables Carta to become a data-driven organization.

We’re a unique and passionate group of engineers, product managers, designers, data engineers, data scientists, and machine learning engineers united by Carta’s mission to create more owners. We place a high premium on experimentation and validated learning and we focus on being values and mission-driven, not didactic or prescriptive. Today, we manage upwards of 1 trillion in equity! 

The Problems You’ll Solve 

As a member of this team, you will be responsible for building and scaling marketing pipelines to make data accurate, accessible and secure. You will build systems that will allow Marketing teams to answer questions they need in a self-service manner, and allow analysts to quickly answer questions about why something is happening in addition to what is happening. You will partner with the rest of the team and with the marketing team to build best-in-class solutions.. Examples of responsibilities will include: 

• Build resilient data pipelines based on internal and external data sources
• Implement tooling for data quality and anomaly detection
• Partner with the rest of the team on prototyping and building scalable products driven by the data team
• Partner closely with marketing team to understand their priorities and help prioritize the work based on their needs
• Proactively communicate opportunities for improvement based on our data
• Constantly identify opportunities for providing self-service tooling to our internal partners 
• Knowledge of marketing field and best practices when it comes to building solutions for the marketing team

The Impact You’ll Have

By building scalable self-service solutions you will enable easier and faster decision making. In addition, you will be able to increase productivity and accuracy of our data team, and operations and product teams as well

About You

Successful candidates in this role will always look for a balance between fast delivery and building for scale.  You don’t follow the status quo but look for ways to improve how we do things.  You are able to talk to technical and business users and explain your work, and are able to be a good partner to your team and to your customers.  Building relationships is a priority. Even though our toolstack (Airflow, DBT, Redshift & Looker) are a good start, you will always be in the know on the latest and greatest technology we could utilize. You concentrate on automation and self service. You are also excited to build new products, starting with ideas and all the way to execution.  Example of problems you will solve include:

• Architecting multi-touch attribution pipeline
• Scaling our data infrastructure and developing software that allows for improved data processing and automation
• Evaluating build vs buy tooling
• Scaling Looker as a platform to solve operational use cases as well as increase self service adoption around the company, especially on the marketing team
• Evaluating and rearchitecting our data model to support existing and future solutions
• Partnering with marketing team to help in data modeling requirements to support analytics
• Understanding the needs of different parts of marketing team, identify pain points and opportunities, and come back with proposals on how data engineering practice can help