Data Engineering Tech Lead

The Team You’ll Work With

The Data Engineering team is responsible for designing and maintaining Carta's data and analytics infrastructure. Our scope is broad and includes building data pipelines, leading end-to-end projects spanning data modeling and visualization, and ensuring the smooth operation of our data infrastructure, which is used by hundreds of Carta employees across business units, R&D, and Data Science and Machine Learning teams. The data infrastructure also serves several Data Products, including Carta’s customer Data Warehouse.

The Data Engineering team is an integral part of the broader Infrastructure Pillar which includes Analytics Engineering and Data Science. We’re a unique and passionate group of engineers striving to enable data driven decision making for our people and our customers!

The Responsibilities Of The Role

As a Data Engineering Tech Lead, you not only are a top contributor, but you are a voice of reason for other senior data engineers, helping navigate ambiguity for other decision makers on the team. You will maintain a high level of individual technical contribution, as the team operates with a significant degree of autonomy and self-governance, making your individual impact as visible as your people management duties. You will be responsible for the people management of a team consisting of 5 senior data engineers.

As a member of this team, you will be responsible for operating and up-leveling our data and analytics infrastructure. You will proactively identify and lead key infrastructure projects to improve stability, observability, and automation. You will play a vital role in data infrastructure procurement processes.

The Problems You’ll Solve

There are numerous problems the data engineering team is constantly working on, you will be expected to directly influence and contribute to all of these:

• Building resilient data pipelines based on internal and external data sources.
• Own and develop foundational infrastructure for NLQ capabilities, ensuring highly accurate and easily accessible data analysis.
• Manage and implement self-servicable, fine-grained data access controls using methods like RBAC, RLS and masking policies.
• Lead major data platform migrations, especially for M&As or next-gen tech adoption.
• Managing complex data reporting, especially with segregated international customer data domiciles.
• Evolve our OLAP infrastructure for internal efficiency and new customer experiences, while optimizing and strengthening existing uses.

About You

• You have 8+ years of data engineering experience building complex data & analytics infrastructure that scales efficiently. 
• You have prior experience as a technical lead in data engineering. Candidates who are new to people management, or have limited experience in this area, are encouraged to apply. 
• You know what it takes to build end-to-end data pipelines, possess strong technical skills and enjoy working with various stakeholders across a fast-growing organization. 
• You’re a strong communicator, embrace ambiguity, and get energized by building thoughtful solutions that move the needle on business goals. 
• You’re not a bystander -- you’re an active participant and have strong opinions on how best-in-breed data and analytics infrastructure should be built. 
• You have experience using Claude Code or any other Agentic AI development tools (e.g. Windsurf, Copilot, Cursor).
• Our data & analytics tech stack is Python, Airflow, dbt, Snowflake, Datahub, Metabase and Hex. Prior experience with these technologies is preferred.
• Some knowledge of and interest in financial technology or 3rd party integrations is great, but not required. 

Salary

Carta’s compensation package includes a market competitive salary, equity for all full time roles, exceptional benefits, and, for applicable roles, commissions plans. Our minimum cash compensation (salary + commission if applicable) range for this role is: 

•  $200,000 - $250,000 in New York, NY

Final offers may vary from the amount listed based on geography, candidate experience and expertise, and other factors.