Hevo Data lives inside AWS, Google Cloud, and Snowflake marketplaces — and the engineers who used to build those pipelines by hand are watching the job postings shift

A $30 Million Bet on No-Code Pipelines

Hevo Data raised $30 million in a Series B round led by Sequoia Capital India, with participation from Qualgro, Lachy Groom, and Chiratae Ventures. The Bengaluru- and San Francisco–based company, founded in 2017 by Manish Jethani and Sourabh Agarwal, has now raised $43 million total. That figure lands differently when you examine what Sequoia India actually backed: a no-code data pipeline platform that shuttles information from CRMs, ad platforms, financial systems, and customer support tools into cloud warehouses like Snowflake, BigQuery, and Redshift.

Sequoia India managing director Tejeshwi Sharma said Hevo builds "foundational infrastructure to enable bi-directional mobility of data" and combines "technical sophistication and consumer grade experience." The company plans to put the capital toward scaling sales and marketing, building new products, and hiring in the U.S. and Europe.

Here's what the bet actually signals. Sequoia India didn't write $30 million to a company selling a marginally better ETL tool. It wrote a check on a thesis: the data integration layer (the plumbing between a company's source systems and its warehouse) is becoming too critical and too complex to leave in the hands of a handful of pipeline engineers writing custom scripts. Hevo's pitch holds that most businesses would rather not staff a large engineering team just to keep data flowing between departments. The no-code wrapper isn't a gimmick. It's the product.

That thesis lines up with demand-side realities. Zero G Talent's board currently lists Hevo openings including a Solution Engineer in Pune, a PLG Growth Manager and Account Executive in Bangalore, and a VP Product Marketing in the U.S. The company is hiring across go-to-market and product simultaneously, evidence that the new funding targets distribution as much as platform development.

The broader data integration market sat at roughly $8.1 billion in 2020 and is projected to reach $17.1 billion by 2026. Hevo's share remains small. But Sequoia India's check says the firm believes the winners in this category won't be the ones with the most connectors or the fastest throughput. They'll be the ones that make the pipeline someone else's problem — and let the analysts, the marketing ops teams, and eventually the agents actually touch the data.

Snowflake Marketplace and the Distribution Moat

In September 2025, Snowflake named Hevo Data a "One to Watch" in the Data Integration category of its Modern Marketing Data Stack report, a designation based on actual usage patterns across more than 11,100 Snowflake customers, not a panel of judges. Denise Persson, Snowflake's Chief Marketing Officer, said Hevo's momentum addressed "the fast-changing needs of modern marketers." The report, its fourth annual edition, assessed adoption across 13 martech and adtech categories using credit consumption, customer counts, and growth metrics over a 12-month window.

Hevo now lives on the Snowflake Marketplace Capacity Drawdown Program, which lets customers buy Hevo directly using existing Snowflake commitments rather than opening a separate procurement cycle. A data team already spending on Snowflake can add Hevo's ingestion layer without requesting new budget, signing a new contract, or running a new vendor review. Billing flows through Snowflake. Hevo activates the subscription after purchase.

This turns a technical integration into a financial one. The Marketplace listing isn't just discoverability — it's a procurement shortcut that shortens the path from evaluation to deployment. The Snowflake Marketplace page lists Hevo as a no-code pipeline platform serving both technical and non-technical users, reinforcing the positioning that earned the "One to Watch" slot in the first place.

The compounding effect is what makes this a moat rather than a badge. Sequoia India's investment signals that a major investor sees structural demand for AI-native data infrastructure. Snowflake's Marketplace availability gives Hevo a distribution channel into the exact customer base already spending on the warehouse layer, the layer that feeds AI workloads. A Snowflake customer building agentic marketing applications doesn't need to be sold on the warehouse; they're already paying for it. Hevo becomes the default ingestion layer they can turn on with existing budget.

For data engineering talent, the implication is concrete: tools that sit inside a dominant platform's marketplace get adopted faster, which means the job postings follow the tool, not the other way around.

What Changes When Every Cloud Has a Buy Button

On October 22, 2024, Hevo Data went live on Google Cloud Marketplace. The move lets Google Cloud customers pull data from over 150 pre-built connectors (including Google Analytics, Google Ads, and Google Cloud Storage) directly into BigQuery. Existing marketplace customers can also apply unused committed spend toward Hevo's services, removing a procurement barrier that slows mid-cycle adoption.

The integration completes a multi-cloud distribution triangle. Hevo already listed on AWS Marketplace and had its Snowflake Marketplace presence bolstered by the "One to Watch" recognition. Adding Google Cloud Marketplace means Hevo now sits inside the procurement and deployment workflows of all three major cloud providers, the same providers where AI workloads actually run.

Dai Vu, Managing Director of Marketplace and ISV GTM Programs at Google Cloud, said the listing lets customers "quickly deploy, manage, and grow the data integration platform on Google Cloud's trusted, global infrastructure." Hevo CEO Jethani framed it as a way for businesses to "maximize their existing cloud investments," language targeting teams already committed to Google Cloud spend rather than trying to win new cloud contracts.

For AI deployments specifically, the BigQuery connection is the critical piece. BigQuery is Google Cloud's primary warehouse for training and serving ML models, and Hevo's no-code pipeline model means data teams can feed it without writing custom ingestion code. The platform handles automatic scaling as data volumes grow, essential when AI pipelines pull from high-velocity sources like ad platforms and product analytics.

The multi-cloud angle compounds the funding signal. A data pipeline vendor that lives inside AWS, Google Cloud, and Snowflake marketplaces becomes the default ingestion layer for teams that refuse to lock into a single cloud, which, in 2024, is most teams running AI workloads.

The question for data engineering talent is straightforward: when the pipeline layer becomes a marketplace commodity that any cloud customer can turn on, what happens to the engineers who used to build and maintain those pipelines by hand?

The No-Code Talent Shift: Redefining Data Engineering

The Fivetran-dbt Labs merger didn't just combine two companies. It drew a line under an entire job category. When the two biggest names in pipeline ingestion and transformation merge into a single platform, the message to hiring managers is clear: the era of staffing dedicated pipeline specialists is ending. Hevo Data's no-code model pushes that shift further and faster than most teams are ready for.

Hevo's platform lets clients integrate data from over 150 sources through a no-code UI, targeting teams that want to move data without maintaining a crew of pipeline engineers. That's not a convenience feature. It's a workforce restructuring tool.

LinkedIn's data engineering community estimates that by 2026, roughly 70% of new enterprise applications will rely on low-code or no-code tools. The implication for hiring is straightforward: demand for traditional ETL developers flattens while demand for people who can orchestrate AI-driven pipelines, manage governance, and interpret outputs rises.

Coalesce's analysis of AI's impact on data engineering spells out where the work goes. AI is embedding itself at every pipeline stage: code generation, automated testing, self-healing observability, real-time anomaly detection, cost optimization, automated lineage. Each capability replaces a task that currently sits on a data engineer's plate. The jobs that remain require a different skill set: less hand-written SQL, more judgment about what the automated systems produce.

Hevo itself posted roles on the same board, including a PLG Growth Manager in Bangalore, a Security and Compliance Engineer, and a VP Product Marketing. The mix tells you what a no-code pipeline company actually hires for: growth, security, product, and customer-facing engineering. Not pipeline builders.

The restructuring isn't limited to Hevo. Databricks added 61 roles there, including a Staff Research Engineer for Data Agents and a Sr. Specialist Solutions Architect for Data Engineering & Warehousing. The job titles reveal the new shape of the field — "Data Agents" and "Solutions Architect" rather than "ETL Developer" or "Pipeline Engineer."

No-code and low-code platforms handle the mechanical work of moving and transforming data. AI handles optimization, testing, and monitoring. What's left for humans is architecture decisions, governance, quality judgment, and the ability to work across systems rather than inside a single pipeline. Data engineering isn't disappearing. It's becoming something closer to AI operations — and the teams that figure out that transition first will have a hiring advantage that compounds.

India's SaaS Resilience and the Global Talent Angle

Hevo's Sequoia India round didn't happen in a vacuum. It landed in an ecosystem that has quietly become the world's second-largest SaaS market by scale and maturity, trailing only the U.S. Indian SaaS companies generated roughly $12 billion in total annual recurring revenue in 2022, a four-fold jump over five years, and pulled in close to $5 billion in investment that same year, according to an ETCIO piece by Hevo co-founder and CEO Manish Jethani.

Hevo fits that thesis precisely. The dual-headquartered San Francisco and Bengaluru company is approaching roughly $44 million to $47 million ARR in 2026. Its no-code ELT platform syncs data from 150-plus SaaS sources into warehouses like Snowflake and BigQuery, targeting mid-market teams that can't fund a large data engineering group. Event-based pricing starts around $149 per month, well below Fivetran's enterprise entry point.

The broader Indian SaaS cohort includes more than 1,600 companies, with 14 above $100 million ARR, Jethani wrote. Communities like Surge, SaaSBOOMi, and Upekkha have built dense founder-to-founder networks that compress the learning curve for new entrants. That infrastructure (talent, peer networks, and a customer base trained on low-friction SaaS buying) means companies like Hevo can scale globally without relocating their engineering core.

When a no-code pipeline tool lets a data analyst build integrations in minutes, the need for a dedicated backend engineer to write custom API scripts shrinks. That's the structural change the Fivetran-dbt merger accelerated: the data engineering role is splitting into a smaller core of pipeline architects and a larger layer of analysts and AI orchestrators who configure pre-built tools. Hevo's own marketing leans into this. A LinkedIn post from Jethani claimed it takes longer to make a drink than to build a Hevo pipeline, a line that signals exactly which job functions the company expects to make obsolete.

For data engineers, the career path that once rewarded deep ETL scripting skills now rewards the ability to orchestrate AI-ready pipelines across multi-cloud stacks. The jobs are moving where the pipelines are built, and right now, that's increasingly India.

The Competitive Landscape: Fivetran-dbt, Databricks, and the AI Data Stack

Fivetran and dbt Labs closed their all-stock merger on June 1, 2026, a deal first announced October 13, 2025, creating a combined entity that serves more than 100,000 data teams globally. George Fraser runs as CEO; Tristan Handy as President. The merged company's first joint product slate (Agents Schema, an open-source context layer for AI agents; dbt Wizard, agent-assisted development; dbt State, caching that skips unchanged builds; and AI Connector Builder, which generates Fivetran connectors from API docs in minutes) is a direct bid to own the full ingestion-to-transformation pipeline for the agentic era.

That consolidation raises the competitive bar for every data pipeline vendor, Hevo included. But the Fivetran-dbt merger doesn't cover the same ground Hevo does. Fivetran's historic strength is automated ELT with strong warehouse-centric delivery; its foray into Iceberg-backed lakehouse destinations is newer. dbt owns transformation and semantic governance. Neither side brings Hevo's bidirectional pipeline model (ETL, ELT, and reverse ETL across 150-plus sources) or its no-code pipeline builder that lets non-specialists configure and maintain flows. The Fivetran-dbt stack targets data engineers who write SQL and manage dbt projects. Hevo's interface targets the adjacent analyst and operations roles who need pipelines without writing code.

Databricks is pushing from a different angle. Its Clean Room architecture, a central, ephemeral environment managed through Unity Catalog and OpenSharing, lets organizations run cross-partner data joins and train ML models without exposing raw data to either side. Databricks' bet is that privacy-safe collaboration becomes the default for enterprise AI, and that clean rooms sit underneath every data-sharing workflow. On Zero G Talent's board, Databricks added 61 roles in the past week alone, including a staff research engineer for data agents in San Francisco and a senior specialist solutions architect for data engineering warehousing. That hiring pace signals where Databricks is placing its engineering talent: agent-facing data infrastructure, not pipeline configuration.

Snowflake, meanwhile, is using its retail media push and Modern Marketing Data Stack partnerships (Hevo is named in that report) to turn the marketplace into a distribution channel for the entire data stack. Hevo's Snowflake Marketplace listing gives it a surface Fivetran-dbt can't easily replicate without going through Snowflake's own partner tiers.

Hevo sits between the Fivetran-dbt engineering-heavy stack and the Databricks governance-first platform, offering a no-code ingestion and transformation layer that plugs into both via marketplace availability. The Fivetran-dbt merger consolidates the top of the funnel for teams that already own their transformation logic. Hevo competes for the teams that don't want to own it, and the Snowflake Marketplace slot gives them a reason not to build internally.

For data engineering job seekers, the practical signal is this: Fivetran-dbt needs people who write dbt models and manage connectors at scale. Databricks needs engineers who build agent-facing infrastructure on clean-room architectures. Hevo's open roles cluster in customer-facing and growth functions rather than deep pipeline engineering. That gap matters when you're choosing which stack to specialize in — because the platform hiring the most pipeline engineers today may not be the one whose tools define the discipline tomorrow.

Working in AI? Zero G Talent tracks the openings: browse AI jobs, openings at Databricks and Hevo Data, and the people building the field.