Senior Data Engineer

AngelList

full-time

Posted on: 9/17/2025

Origin: • 🇺🇸 United States • California

✨ AI Apply

💰 $190,000 per year

Senior

AirflowAmazon RedshiftAWSCloudDistributed SystemsDockerETLKubernetesNoSQLPythonRubySQL

About the role

Design, build, and maintain robust, scalable, and efficient ETL/ELT pipelines for structured and unstructured data.
Implement best practices for data ingestion, transformation, and integration across multiple sources.
Optimize and maintain data storage solutions (e.g. SQL/NoSQL databases, data lakes, warehouses) for performance and cost-effectiveness.
Collaborate with data analysts and business stakeholders to understand data requirements and deliver solutions that meet their needs.
Contribute to the development and implementation of the organization’s data architecture and strategy.
Continuously evaluate and integrate new technologies and tools for automated data quality validation and to improve data engineering processes overall.
Maintain and improve robust monitoring and alerting mechanisms for data pipelines and systems.

5+ years of experience designing and implementing scalable data pipelines and infrastructure.
Strong technical expertise in modern tools and frameworks, including cloud platforms (we're built on AWS), distributed systems, and advanced SQL.
Proficiency in programming languages (such as Ruby or Python)
Hands-on experience with data warehousing solutions like Redshift or Snowflake and orchestration tools like Airflow or dbt.
Proactive problem solving with a track record of optimizing systems for performance and cost while addressing complex technical challenges.
Excellence collaborating with cross-functional teams, aligning technical solutions with business objectives, and delivering measurable improvements to data workflows and decision-making processes.
Startup/high-growth experience with the scrappiness required to be successful at AngelList!
Other nice-to-have: Experience with containerization (Docker, Kubernetes) and CI/CD pipelines.
Other nice-to-have: Exposure to machine learning workflows and supporting MLOps tools.
Other nice-to-have: Experience in building analytical tools or data engineering services.