| ▲ | lpandong 7 hours ago | |
Tiger Data (FKA Timescale) [ Global | 100% Remote | Multiple Roles: Eng, GTM, Support + more ] We began in 2017 as TimescaleDB, focused on making PostgreSQL the best database for time-series data. As our customers evolved, so did we. Today, more than 2,000 companies use our technology to power real-time analytics, agentic applications, and high-ingest operational systems. What started as a time-series database has become the fastest PostgreSQL for modern workloads. To reflect that evolution, we’re now TigerData—a name that speaks to our speed, precision, and ambition. The tiger has long been part of our culture: fast, focused, powerful. From “Tiger Time” all-hands to welcoming new hires as “tiger cubs,” it’s how we build—decisively, fearlessly, and with purpose. This is not a reinvention: it’s a reflection of how we already serve our customers today. TigerData is growing! Check us out: https://www.tigerdata.com/careers Senior Platform Engineer - Postgres Specialist https://www.tigerdata.com/careers/8c701831-c4cf-4cc8-a9f0-0a... Customer Delivery Engineer - https://www.tigerdata.com/careers/3d060bc0-1abf-446b-881e-f4... Partner Sales Manager, AWS - https://www.tigerdata.com/careers/c4980e2e-3b9e-4f97-9a2a-cf... | ||
| ▲ | dataviz1000 6 hours ago | parent [-] | |
I use TimescaleDB every day. Being able to ask questions to an LLM copilot about patterns across several billion rows, 140GB of time series data, like how the market reacts 15 minutes, 1 day, and 1 week after Federal Reserve press releases, using time_bucket() and other aggregation functions with compressed chunks, and having the copilot generate the queries and run them fast, is going to be game changing. I can ask to write a script to scrape the press releases, to run them through another agent to classify them (hawk, dove) and use that information in the time series analysis. Even though that exploration has not produced any actionable, tradable results for me, which is why I am sharing it, eventually TimescaleDB will be one of the most important tools in fields like medical research. A researcher will be able to ask in natural language for statistically significant patterns in the data, and LLM agents will query it quickly while taking advantage of all the TimescaleDB optimizations. | ||