StarTree
startree.ai
Actively hiring
Data Warehouses & Lakehouses
Stream Processing & Real-Time Data
View 4 jobs
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Overview
News
Technologies
Salaries
Products
People
Growth
Offices
Jobs
Financials
Funding and backers
Total raised
$71M
2 rounds on record
Latest round
$47M
Venture round · Jul 2022 · 4 yr ago
Team
51-200
employees, estimated range
Hiring now
4
open roles
Backers
3
CRV, Notable Capital
The money's age
Long since its last round
50 months since the last round · hiring slowed
Momentum
25/100
a new city, in the news
Last sign of life
this month
a posting or a news item
The story so fareach stage dated by its source: the company's releases, the press, registers, filings
Jul 19, 2022
Raised a round Venture round $47M · SEC Form D
May 5, 2021
Raised a round Series A $24M · ZDNET
2019
Founded
Tractionpublic signals, read monthly
Web presence
top 0.41%
of 133.2M domains by links, Common Crawl
Hacker News stories
13
best: 67 points
Funding rounds
| Round | Date | Amount | Investors | Source |
|---|---|---|---|---|
| Venture round | Jul 2022 | $47M | — | SEC Form D |
| Series A | May 2021 | $24M | — | ZDNET |
Registers and programsofficial lists and records that name the company
| Legal entity | StarTree, Inc. | SEC Form D |
Founders and leadersfrom filings, registers and the company's site
Kishore GopalakrishnaExecutive Officer, DirectorSEC Form D
Backed byfrom the funds' portfolios and accelerator catalogs
CRV 
Notable Capital 
Sapphire Ventures Investors in similar startupsfunds that back the companies most like this one
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Overview
StarTree is a managed real-time analytics platform designed for OLAP at massive speed and scale, built on Apache Pinot. It aims to make it simple for companies to delight internal and external users and create new revenue streams from their data, and it hires technical support, data platform, cloud platform and site reliability engineers, including in Bengaluru. The company is headquartered in Mountain View, California, United States.
News
Faster, Safer Apache Pinot Restarts with Pool-Based Instance Assignment
As an Apache Pinot cluster grows from a handful of servers to dozens or hundreds, routine operations such as version upgrades and server maintenance start to require more planning. Restarting servers one at a time is a safe approach, but on a sufficiently
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Report
Inside the flight path of realtime ingestion in Apache Pinot (Part 3)
In Part 1 we followed the bytes. Kafka partitions, the consume loop, mutable segments, flush thresholds and the immutable build.In Part 2 we followed the agreement. Servers report their offsets. The controller picks one committer and one winning offset. A
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Report
Scaling Apache Pinot Beyond Millions of Segments (or Parquet Files)
Apache Pinot is built for real-time analytics at very large scale. Production clusters can hold hundreds of terabytes to petabytes of data, ingest millions of events per second, and return queries in milliseconds. For most deployments, scaling Pinot is st
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Report
Materialized Views vs Indexes vs Star-tree Index
Materialized views are one of the oldest tricks in database engineering. Oracle shipped them in the 1990s. Postgres has had them for over a decade. Every data warehouse worth its salt supports some flavor of pre-computed, persisted query results. There is
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Report
Designing Apache Pinot for High Availability in the Cloud
In May 2026, AWS reported a thermal event affecting infrastructure in a single Availability Zone. EC2 instances and EBS volumes on impacted hardware were impaired, and AWS shifted traffic away from the affected zone for many services. That is exactly the
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Report
Full-Text Search on Iceberg: One Engine for Analytics and Search
Apache Iceberg has become the default table format for the lakehouse. Analytics, reporting, ML feature pipelines, streaming, and batch all read and write the same Iceberg tables - one copy of the data, in open Parquet, in your bucket, queried by many engi
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Report
The Old-Gen Cliff: Lessons from a High-QPS Pinot MSQE Join POC
Joins are not new. Analytical databases have supported them for decades. What is still hard is running complex joins at high concurrency with tight latency goals - especially in real-time analytics systems where fresh data is continuously arriving. That i
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Report
Engineering the Log Lakehouse
Log analytics has become one of the most expensive and operationally painful parts of the modern data stack. The ELK stack and Splunk were built from a search-first design center. That design made sense for the original problem: index machine data aggress
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Report
Databricks Lakehouse//RT vs. StarTree
Today, Databricks announced Lakehouse//RT, a real-time data warehouse powered by a new engine called Reyden. The pitch: millisecond query performance directly on governed Delta and Iceberg data, no separate serving layer, no data movement. We have one rea
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Report
Sub-Second Analytics on Delta Lake - Without the Workarounds
Delta Lake is the format of choice for teams running Databricks at scale. But one class of analytic workloads has never quite been at home in the data lakehouse: the ones with SLAs. When an on-call engineer is waiting five seconds for an observability das
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Show more news
Tech stack
Technologies named in job postings at StarTree over the last 12 months, by layer, with the share of postings for each.
Technologies in use
28
Postings analysed
6
Stack modernity
76/100AI adopter
Languages
Java
SQL
Python
Bash
Frameworks & libraries
Hadoop
Databases
ClickHouse
Druid
Presto
Trino
Cloud & platforms
Kubernetes
Linux
AWS
Azure
AI & ML
LLM
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Full StarTree tech stack
28 technologies · by team · pay by technology · changes · similar stacks
People
Kishore Gopalakrishna
Executive Officer, Director
Enrique Salem
Director
Max Gazor
Director
Glenn Solomon
Director
Names and roles come from public filings, company registers and the company's own site. Is this you? Correct or remove · How we handle this data
Growth
Hiring Momentum
59/100
Growing
Open positions
4
0 opened / 2 closed in 30 days
Median time-to-fill
56 days
faster than 25% of the market
ATS activity
Every ~4 hours
Today
Hiring Dynamics
+100%
Hiring Focus
The percentage next to each role is its share of the company's job openings over the last 90 days; the arrow shows the shift versus the previous period.
Backend
57% ▼
Support
29% ▲
DevOps
14% ▼
Activity Timeline
New location: Bengaluru
Sep 2026
Added TCP/IP to stack
Sep 2026
Added DNS to stack
Sep 2026
Added Apache HTTP Server to stack
Sep 2026
Added Unix to stack
Sep 2026
Added Linux to stack
Sep 2026
Added Terraform to stack
Sep 2026
Added Stripe to stack
Sep 2026
Added LinkedIn to stack
Sep 2026
Added Together AI to stack
Aug 2026
Added Splunk to stack
Aug 2026
Added LLM to stack
Aug 2026
Offices
Where the company hires and what each office is for
Cities
2
Hiring now
1
Countries
2
Busiest office
Bengaluru
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Headquarters
Builds here
Office with open vacancies
No open vacancies right now Tap a pin for the office
Mountain View
United States
HQ
Connect with us
Mountain View, CA 94041
Bengaluru
India
Engineering / R&D
Bengaluru
4 open roles 4 posted in 90 days 75% engineering 75% remote 50-200 est. staff
hires for Backend, Support
Jobs
≈ $12k – $25k per year (Estimated) • Remote (United States, India, UTC-8 – UTC+5:30 hours) • 4+ years exp • Bachelor's Degree • Bengaluru
SQL
Druid
ClickHouse
Presto
Apache Kafka
Trino
AI/ML
Hadoop
Spark
DevOps
GCP
Azure
AWS
Kubernetes
Linux
Management
Slack
Stripe
Apply
≈ $20k – $44k per year (Estimated) • Remote (India) • Bengaluru
Java
LLM
Together AI
DevOps
Splunk
Cybersecurity
Crowdstrike
Management
Stripe
Apply
≈ $19k – $44k per year (Estimated) • In office • Bengaluru
Java
Together AI
DevOps
Terraform
GCP
Azure
AWS
Kubernetes
Management
Stripe
Apply
Remote (India) • 1+ year exp • India
Python
Bash
GCP
Azure
CI/CD
AWS
Docker
Kubernetes
Linux
TCP/IP
DNS
Apache HTTP Server
Management
Stripe
Apply