About AngelList:
We exist to accelerate innovation by increasing the number of successful startups in the world. We want to give more people the opportunity to participate in the venture economy by building the financial infrastructure that makes it possible for more people to invest in world changing startups, and build tools for startups that help them run their operations so they can focus on building.
AngelList is the nexus of venture capital and the startup community. We support over $124B+ assets on our platform, and we’ve driven capital to over 12,000 startups, almost 282 of which are unicorns. 57% of top-tier U.S. VC deals involve investors on AngelList.
While our scale is large, our ambitions are even larger – we’re innovating on the financial infrastructure for venture investors and the startups they invest in. Come build with us.
About the Role:
AngelList is hiring a Senior Analytics Engineer who is passionate about using their technical skills to democratize data, metrics, and insights for the team. Reporting to the Head of Data, you will play a pivotal role in designing, building, and maintaining scalable data pipelines – ensuring efficient data flow and integration to support analytical and operational efficiency. With a highly skilled and experienced Data/Analytics Engineer, we can accelerate the development of reliable pipelines, improve data availability, and empower faster decision-making across teams!
If you're enthusiastic about working in a startup environment with large datasets, optimizing data systems, and enabling data-driven decision-making, we’d love to hear from you.
Key Areas of Impact:
- 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.
Ideal Candidate will Bring:
- 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) – you don't need to be a software engineer, but should understand our codebase.
- 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 characteristics that are nice-to-have:- Experience with containerization (Docker, Kubernetes) and CI/CD pipelines.
- Exposure to machine learning workflows and supporting MLOps tools.
- Experience in building analytical tools or data engineering services.
How Success will be Measured:
- Delivery of reliable, scalable, and efficient data pipelines within agreed timelines, with minimal downtime and robust monitoring systems in place.
- Successful collaboration with analysts and stakeholders to enable seamless access to accurate, timely data for decision-making.
- Tangible improvements in infrastructure scalability, cost-effectiveness, and adaptability to evolving business needs.