Alpaca is a series A fintech startup that’s raised over $21M in funding. Alpaca builds an API for stock trading that enables developers across the world to build trading algos, applications, and brokerage services.
Alpaca’s globally distributed team consists of developers, traders, and brokerage specialists, and is backed by a group of prominent investors including Spark Capital, Portag3, Social Leverage, Elefund, Y Combinator, and highly experienced industry angel investors. We are also a very tech-enthusiastic team with notable track records in open source and startup communities.
We are looking for a senior leadership of Data Engineering who designs and develops the data management layer of our platform and build a talented team around it. The data management at Alpaca spans across financial transactions, customer data, API logs, system metrics that affects both internal and external user's decision making. As our business grows, the complexity and volume of data ever grows, from billions of data points in a month today.
You are supposed to have:
- 7+ years experience in software engineering
- 3+ years experience in building data warehouse/data lake platform
- Strong math skills to understand the scale and build the solution at the high level and low level quickly
- Strong knowledge around cloud environment including Kubernetes and Docker
- Strong knowledge of relational database systems (especially PostgreSQL)
- Software engineering background in one of the major programming languages
- Deep knowledge around problems and solutions around distributed system, storage, transactions, query processing
- Low to high level skills around handling customer data, metrics, and transactions, including SQL, R, Hadoop, BI tools, and data security
- Production experience with at least one ETL pipeline platform (dbt, airflow, nifi, etc)
- Nice to have: Experience with message queuing or streaming platforms and architectures
- Robust understanding of timeseries data problems
You will be doing:
- Understand the today's and tomorrow's problem of the data layer and design the solution
- Implement the solution from scratch, and extend it as the business scales
- Plan the sizing of the system for 1, 3, 6 months ahead
- Operate the system and handle the production issues in a timely manner
- Build and manage a functional team which may span across multiple timezones
- Work closely with sales, marketing, product, operations to understand the data needs
Alpaca
