17,298 active roles, straight from company career pages.
With 17,298 active listings on JobFerret, the data and AI space is one of the most active hiring categories in tech right now. But browsing these roles quickly reveals something more interesting than raw volume: the lines between job titles are being redrawn in real time. The AI hiring wave has not simply added new roles on top of the old ones. It has pushed employers to rethink what they actually need, and that tension shows up directly in how positions are written and what tools they require.
The classic split between the data analyst and the data engineer still exists, but it is blurring fast. Analysts were once defined by SQL, spreadsheets, and dashboards. Engineers owned the pipelines, the warehouses, and the infrastructure. Today a significant slice of listings ask for both in the same breath, often under titles like Analytics Engineer or Data Platform Analyst. Then there is the machine learning layer. ML Engineer, MLOps Engineer, AI Engineer, and Applied Scientist are all live titles competing for similar candidate profiles, each reflecting a different organizational theory about where modelling ends and production begins. If you have been confused about which lane to pursue, you are not alone, and the market itself has not fully settled the question.
Tooling is one of the clearest signals in the listing data. Databricks appears directly as a top hiring company with 101 active roles, which is notable because it means the platform vendor itself is aggressively recruiting the practitioners who use its stack. Across the broader listing set, cloud-native tools, dbt, Spark, and Python dominate the requirements text, while LLM-adjacent skills including prompt engineering, vector databases, and retrieval-augmented generation are appearing with increasing frequency in roles that would have been called plain data engineering a year ago.
Among roles with a stated location, the geographic spread is wide. Worldwide remote listings (184) and US-based listings (130 and 81 under slightly different location strings) lead the count, with India (70) also representing a meaningful share of explicitly located postings. Across all 17,298 listings, 30% are tagged as remote, giving candidates real flexibility regardless of where they are based.
On the employer side, agency (748 roles) and bjakcareer (308) are the largest sources by listing volume, reflecting recruiter and staffing activity at scale. Alongside them, Databricks (101), Turner Townsend (100), Nebius (86), and SpaceX (74) represent a mix of hyperscalers, infrastructure firms, and high-profile engineering organizations all hiring in this space simultaneously.
Data engineers primarily build and maintain the pipelines, storage systems, and transformation layers that make data usable. ML engineers take that data and focus on training, deploying, and monitoring machine learning models in production. In practice, many current job listings blend these responsibilities, especially at smaller companies or teams building AI products from scratch.
Python, SQL, and cloud platforms form the consistent baseline across analyst, engineering, and ML roles. Spark and dbt appear heavily in engineering-focused listings, while roles with an AI or LLM focus increasingly mention vector databases, model fine-tuning frameworks, and orchestration tools like Airflow or Prefect.
30% of the 17,298 active listings are tagged as remote. Among roles with a stated location, worldwide remote and US-based positions account for the largest share of explicitly located postings, suggesting meaningful flexibility exists across seniority levels and specialisations.