Full-Time

Staff Software Engineer

India

Updated on 9/4/2026

Altimate.ai

Altimate.ai

11-50 employees

Enforces data contracts at data sources

No salary listed

Bengaluru, Karnataka, India

Hybrid

Hybrid work arrangement in Bengaluru.

Category
Software Engineering (2)
,
Required Skills
LLM
Kubernetes
Microsoft Azure
FastAPI
Python
React.js
Git
SQL
Machine Learning
Data Engineering
Docker
RAG
Microservices
AWS
REST APIs
Google Cloud Platform

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Requirements
  • 10+ years of experience building Python-based web application backends and the infrastructure on which they run.
  • Extensive experience building scalable back-end systems, application programming interfaces, and microservices.
  • Deep understanding of cloud platforms including Amazon Web Services, Microsoft Azure, and Google Cloud Platform, as well as containerization technologies including Docker and Kubernetes.
  • Experience with FastAPI for building high-performance application programming interfaces.
  • Familiarity with artificial intelligence and machine learning concepts, particularly Large Language Models.
  • Strong proficiency in Structured Query Language, including query profiling, optimization, and performance tuning.
  • Experience with Structured Query Language Abstract Syntax Tree analysis and working with Structured Query Language parsers such as sqlglot.
Responsibilities
  • Lead the development and maintenance of backend infrastructure powering Large Language Model architectures, including Retrieval-Augmented Generation, ReAct, and agent-based systems.
  • Architect data pipelines for processing extensive datasets and develop artificial-intelligence-powered endpoints for the Software as a Service application and Visual Studio Code extension.
  • Design and develop large-scale graph-based data structures that model complex data stores and provide context to artificial intelligence applications while ensuring scalability and performance.
  • Lead the design and development of a SQL intelligence system covering query optimization, dynamic pipeline generation, and data lineage tracking.
  • Use SQL query profiling, Abstract Syntax Tree analysis, and parsing to improve query performance and implement granular column-level lineage.
  • Contribute to open-source initiatives.
Desired Qualifications
  • Experience with FastAPI for building high-performance APIs.
  • Familiarity with AI and machine learning concepts, particularly in the context of LLMs.
  • Strong proficiency in SQL, including query profiling, optimization, and performance tuning.
  • Experience with SQL AST analysis and working with SQL parsers such as sqlglot.

Altimate.ai provides data management solutions that help organizations prevent data quality and access issues. It enforces data schemas and expectations directly at data sources using data contracts and AI-assisted tooling, enabling data consumers to set guardrails without needing deep technical expertise. The platform integrates with enterprise data environments to reduce expensive backfilling and cleanup by preventing problematic data before it enters downstream systems. Altimate.ai differentiates itself by focusing on source-of-truth data contracts and automated governance, rather than post-hoc cleaning, positioning its products as category-defining tools for enterprise data and AI needs. Its goal is to make reliable, well-governed data broadly accessible to enterprises worldwide, backed by strategic investors and advisors in the San Francisco Bay Area.

Company Size

11-50

Company Stage

Seed

Total Funding

$2M

Headquarters

Sunnyvale, California

Founded

2022

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Simplify Jobs

Simplify's Take

What believers are saying

  • July 28, 2026 private preview users saved 19% on warehouse costs, validating ROI.
  • Altimate Lite's $100 monthly trial and credits-based billing lower adoption friction for buyers.
  • May 2026 Datamates docs show integrations with Databricks, Snowflake, Jira, and 35-plus model providers.

What critics are saying

  • Snowflake can copy cost-optimization features fast, crushing Altimate's differentiated app layer within months.
  • LinkedIn shows only 11-50 employees on September 1, 2026; execution breaks after one miss.
  • If Snowflake Marketplace demand stalls, Altimate's native-app wedge loses distribution and the company starves.

What makes Altimate.ai unique

  • July 28, 2026 Altimate Lite runs inside Snowflake, avoiding data egress and security reviews.
  • Altimate Code, updated September 6, 2026, embeds 100-plus deterministic tools for data engineering tasks.
  • Datamates unifies dbt, Snowflake, Airflow, Cursor, and GitHub Copilot into one agentic workflow.

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Benefits

Company Equity

Professional Development Budget

Growth & Insights and Company News

Headcount

6 month growth

12%

1 year growth

12%

2 year growth

7%
Altimate AI
Jul 28th, 2026
Autonomous agents for Snowflake Cost Optimization: announcing Altimate Lite.

Autonomous agents for Snowflake Cost Optimization: announcing Altimate Lite. Snowflake and AI costs can spiral out of control. Altimate Inc. introduce Altimate Lite for AI and Warehouse Cost Optimization, an easy solution to track and contain Snowflake costs. Now live on Snowflake Marketplace. Updated July 28, 2026 TL;DR: it saves you money on Snowflake. Altimate Lite is a Snowflake native app that automatically tunes warehouse configuration (auto-suspend, scaling, clustering) roughly every 5-6 seconds to cut compute costs, with no performance impact. Private preview customers saved an average of 19% on warehouse costs, with some seeing savings as high as 57.5%. As a Snowflake native app, it's easy to adopt, secure, and can be paid with Snowflake credits. It's free for 30-days, then just $100/month plus 2.5% of the daily cost of any warehouse you activate it on. Watch the launch video. How AI costs get out of hand on Snowflake. If you think your Snowflake instance is costing more than it should, you are not alone. There are several factors that drive this: First, a common pattern keeps showing up across Snowflake teams that adopt AI: everything starts to look like an AI problem. Teams transition away from cheap deterministic tooling and start to adopt inference-based solutions across the board. This gets expensive. Then there is the "Maximilian Fable" persona: always reaching for the newest, most expensive reasoning model. Tasks that a lightweight model like Anthropic's Haiku would handle just fine often get clobbered with one of the most expensive models like Fable. Snowflake offers some tooling for tuning your instance, but the process is manual and the audits too far apart to get good results. Snowflake users overspend as a result. Finally there's cost visibility. Most teams just get a bill at the end of the month, a token count with no breakdown of what drove it. It's a single total, no itemization, and no way to explain to finOps why this week's number is double last week's. These costs really add up. There's now a well-known case of a company running up a $500 million AI bill in a single month because no check was in place and nobody saw it coming. The fix starts with tracking and visibility: who's consuming AI credits, whether usage is concentrated in a handful of users, and which models and services are actually driving spend. Continuous auto-tuning: the other lever for cutting Snowflake warehouse costs. Cost isn't just an AI-model problem. It's also a Snowflake warehouse configuration problem. Snowflake exposes a handful of levers: warehouse size, warehouse type (Gen 1, Gen 2, adaptive), auto-suspend timing, multi-cluster scaling, and max concurrency level. Getting these right, and keeping them right as workloads shift, is normally a manual, periodic exercise: someone looks at usage patterns, makes a best guess, and revisits it weekly or monthly. What if software made these decisions continuously instead? Not daily reconfiguration but adjusting configuration every five to six seconds, a frequency no human team could sustain. By comparison, Snowflake's own auto-suspend only probes warehouse activity every 30 seconds; in that same window, Altimate's auto-tune has already run roughly ten checks, looking at idle state, workload shifts, and predicted upcoming load. Real world results: up to 57.5% in Snowflake warehouse savings. Over the last couple of months during private preview, real Altimate Lite customers saw meaningful savings simply by turning on auto-tune, with no degradation in query performance or queue times: * Lowest savings observed on any warehouse, any day: 12.4% * Average savings across all customers, warehouses, and days: 19% * Highest single-day savings (typically weekends, when workloads are less consistent): 57.5% Inside Altimate Lite: cost dashboard, auto-tune, and AI cost observability. Altimate Lite is a distilled version of Altimate's enterprise platform that runs natively on Snowflake. Is was trained on billions of config changes from years of production use. Because it runs entirely inside your Snowflake environment, no data leaves your account, which sidesteps the InfoSec review that a SaaS tool would typically require. In the demo, Pradnesh walked through the core screens: * Cost dashboard: trending costs, potential annual savings, and how many warehouses are auto-tune eligible vs. already enabled. * Per-warehouse view: estimated 30-day cost, potential (or realized) savings, and a one-click toggle to turn auto-tune on or off. * Decision history: a full audit trail of every action taken (cluster suspensions, resize decisions) timestamped and exportable as CSV. On a single warehouse, the system logged 118 tuning decisions in one day. * AI cost observability: a separate view tracking spend, token usage, and adoption across roughly eight AI services (AI SQL functions, Cortex, Code CLI tools, and more), filterable by service, user, and model, with CSV export for further analysis. Daily AI cost chart showing spend trends over a one-month period. Per-user AI usage showing cost, tokens, queries, and daily spend for a single Snowflake user. Altimate Lite pricing: free 30-day trial, then $100/Month. Altimate made a deliberate choice to keep pricing transparent and self-serve. No sales call required: * 30-day free trial, no cost. * $100/month flat fee after the trial. * 2.5% of daily warehouse cost (after savings) for each warehouse you activate auto-tune on. You only pay for the warehouses you turn on, regardless of how many others exist in your account. The recommended path: turn it on for one or two warehouses, watch the savings and audit history for a bit, and expand from there once you're comfortable. Altimate Lite vs. Enterprise: what's the difference? Altimate Lite covers auto-tune and AI cost visibility. The Enterprise edition, which has been running in production for customers processing billions of queries, adds: * Auto-resize: automatic warehouse right-sizing as workloads shift. * Altimate AI Studio: a collaborative agent workspace for deeper savings analysis, scheduled reporting, and team-based showback/chargeback with SSO integration. * Assisted optimization: guided recommendations for improving query code, table and storage configuration, and dbt models, safely assignable to teams. * Altimate Code: an open-source CLI and VS Code extension that catches costly SQL and dbt mistakes during development. On data handling: Enterprise is a hosted SaaS product that analyzes query history and telemetry (not your underlying business data) to power these features. The Altimate platform is SOC 2 compliant and has been through security review with financial services and healthcare customers, including in Europe. Altimate Lite: key questions summary: Is Altimate Lite free to try? Yes. Altimate Lite includes a 30-day free trial with no cost. After that, it's $100/month plus 2.5% of the daily cost of any warehouse where you've enabled auto-tune. Does my data leave my Snowflake account? No. Altimate Lite runs entirely inside your Snowflake environment as a native app, a walled garden, and none of your data ever leaves your account. How much can I save? Private preview customers saved an average of 19% on warehouse costs, with a low of 12.4% and weekend peaks as high as 57.5%, with no measurable impact on query performance. How is this different from Snowflake's built-in auto-suspend? Snowflake's auto-suspend checks warehouse activity every 30 seconds. Altimate's auto-tune runs roughly ten checks in that same window and adjusts configuration every five to six seconds based on predicted workload. How to Get Started with Altimate Lite's Free Trial Altimate Lite is live now on the Snowflake Marketplace. Start your 30-day free trial today. If you want to talk through auto-resize, Enterprise features, or just how to think about AI cost management more broadly, reach out to the Altimate team, no obligations, no high-pressure sales.

Australian Financial Review
May 8th, 2025
Alvia Asset Partners Acquires Altimate Stake

Brisbane's Alvia Asset Partners, managing investments of $650 million for around 60 family offices and non-profit clients, has acquired a stake in Altimate, Australia's largest ice-cream cone manufacturer.

AiThority
Sep 23rd, 2024
Altimate AI Launches Datamates - AI Teammates for Data Teams

Altimate AI launches datamates - AI teammates for data teams.

Temcom
Jan 18th, 2024
Kontron acquires a majority stake in Katek

In September 2023, Kontron announced that it had acquired Altimate, a Romanian company specialising in urban and interurban mobility solutions, for €11.64 million.