2026-07-01
Data Engineer / Scientist: 2,975 open roles, $185k median — the infrastructure layer for the AI era
2,975 open Data Engineer and Data Scientist roles. 1,010 companies. Median $185k USD. The data engineering market in July 2026 — from live ATS data.
Every AI product runs on clean data. The companies that win in the AI era will be the ones that got their data infrastructure right first. That's why data engineering is one of the largest and most sustained hiring markets in tech.
I track Data Engineer and Data Scientist roles daily from public ATS feeds. Here's July 2026.
What Data Engineers and Data Scientists do
Data Engineers build the pipelines: ingestion, transformation, storage, and serving. The stack typically involves Spark, dbt, Airflow or Prefect, Kafka, and cloud data warehouses (Snowflake, BigQuery, Databricks). The job is to get data to the people and systems that need it, reliably and at scale.
Data Scientists use that infrastructure to build models, run experiments, generate insights, and — increasingly — fine-tune and evaluate ML models that power AI products. The boundary with ML engineering is blurry and getting blurrier.
Both roles have acquired new urgency in the AI era. Feeding an LLM or an agent with the wrong data produces wrong outputs. Building RAG systems requires vector embedding pipelines. Fine-tuning requires curated, clean training data. The data team has moved from "supporting function" to "critical path for AI product success."
The data: 2,975 open roles across 1,010 companies
2,975 active roles. 1,010 companies. 355 new roles in the last 7 days.
1,010 companies is the widest employer base of any niche we track alongside product management — data engineering is truly cross-industry. 355 new openings per week is the second-highest weekly volume after product management.
Who's hiring most aggressively
| Company | Open Data roles |
|---|---|
| Speechify | 242 |
| Accenture Federal Services | 39 |
| Brillio | 31 |
| Truelogic | 30 |
| OpenAI | 29 |
| Airwallex | 25 |
| Novartis | 24 |
Speechify at 242 is the headline number — an audio AI company with an enormous data infrastructure build-out. Brillio and Truelogic are data consulting shops aggregating client demand. OpenAI at 29 data roles reflects the reality that even frontier AI labs are still building out their data infrastructure layer — training data curation, telemetry pipelines, evaluation datasets.
Novartis at 24 signals that pharma has become a serious data engineering employer — drug discovery, clinical trial data, and regulatory reporting all require sophisticated data pipelines.
What they pay
Of 2,975 roles, 1,019 (34.3%) have a published salary range. Among USD roles:
- Median: $185,000/year
- 25th percentile: $167,500
- 75th percentile: $225,000
A tight P25-P75 range ($57k spread) relative to engineering niches with wider variance. The data engineering market has mature compensation bands — companies know what this talent costs and price accordingly. The $185k median has been relatively stable over the past 18 months, unlike ML/AI which has continued to appreciate.
Freshness: 355 new roles in 7 days
Sustained, high-volume demand. The data market isn't growing at the explosive rate of agentic or ML engineering, but it's not slowing either. It's the bedrock layer.
What this tells us about the data engineering market
1. 1,010 companies means data is truly cross-industry. Finance, pharma, retail, AI labs, logistics — if you have a digital product, you need data infrastructure. This is the broadest employer base in our database. The implication: data engineers have real leverage in choosing sector and work environment.
2. Consulting/staffing accounts for a significant fraction of demand. Brillio, Truelogic, Accenture — the data consulting channel is large. Many enterprise data projects run through consulting shops before being brought in-house. If you're early-career, data consulting is a fast way to get cross-industry experience.
3. The AI adjacency is real and accelerating. OpenAI hiring 29 data engineers is the signal. AI companies don't just need ML engineers — they need data engineers who understand how to build training pipelines, evaluation datasets, and feature stores for ML systems. "Data engineer who understands ML systems" is the premium sub-role.
4. $185k median is stable but the AI premium is at the top end. Senior data engineers with ML infrastructure experience at AI labs or AI-native product companies are clearing $250k+. The median understates the ceiling for people who can sit at the data/ML intersection.
The board
latchhire.com/board/data — updated daily. Every role links to the original posting.
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Data pulled 2026-07-01. Active roles only. Salary USD only. "New in 7d" = first seen in the past 7 days.