I'm delighted to announce that I’ve joined Bluesky as part of the founding Engineer team. I wanted to share a few words about why I’m so excited about the company. In a nutshell, I think Bluesky has the opportunity to become one of the truly great companies defining next-gen AI-driven data infrastructure. There are a lot of interesting technical challenges to bring this vision into reality, but I’m ready to dig in and build.
A Big Opportunity for Impact
In many ways, joining Bluesky feels like a logical culmination of the last two decades of my career. I have worked in distributed systems for over 14 years. In that time, I got first hand experience building out next-gen big data infrastructure for some of the world’s most data-driven businesses. Most recently I was at Netflix, working on the Big Data platform team, where I focused on workflow orchestration. Previously I spent five years at Uber managing the Batch Compute team to support ML and Big Data Analytics. (Uber is also where I met and worked with Zheng Shao, Bluesky’s co-founder.) I was also a tech lead on the Hadoop Yarn project. Scaling and optimizing big data infrastructure was a core challenge at both companies.
I believe the future of data infrastructure relies on ML and AI. I am passionate about working at the intersection of big data infrastructure technologies, workload optimization and cost governance. I’m most excited about the semantic optimization layer that Bluesky is developing. It is going to revolutionize the optimization layer and radically improve how companies run their analytical workloads on modern data clouds.
Workload Optimization to Achieve Data Efficiency
If you’ve been paying attention to the data cloud ecosystem, you know that managing cloud spend can be a challenge. Snowflake, for example, offers relational, scale-out capabilities to make data available at a lower cost with fewer data management overheads. However, the continuous influx of data leaves organizations with the baggage of unpredictable compute costs while performing analytics on growing enterprise data. As companies increasingly run data cloud workloads, it has become more challenging to properly attribute, monitor, and find surplus workloads to turn off to be more efficient. That’s where Bluesky comes in. Our mission is to make data efficient and simple, and to make data drive more business value, faster.
Ultimately proper optimization of your data cloud environment is the only viable route to preserving resources as you scale. Bluesky’s unique approach to workload optimization enables data teams to skip the tedious manual trial and error process of tuning and leverage an intelligent and automated solution to quickly find optimal layouts and warehouse settings. There is no doubt in my mind that 1,000s of data-driven companies worldwide will benefit from Bluesky’s innovation and data engineers will wonder why they ever did things differently.
Bluesky is a remote-first company. The culture is analytical, data-driven and transparent. It is also super collaborative and fun.
We strive to have the best data DNA in the industry. Our entire team across product management, marketing, sales and engineering have spent many years in data management, data analytics, machine learning, AI and cloud. The company is backed by top investors, including Greylock and several industry angels. Here are a few articles about the company so far:
- Snowflake Optimization Startup Bluesky Launches with $8.8M in funding
- Startup Spotlight: Bluesky
- Bluesky Built Cost Guardrails to Help Cut Snowflake Spend
We’re hiring. If you are looking to make an impact and inspired to build the future AI-driven data infrastructure then take a look at our job openings.