Fill Rate Intelligence: How Long Does It Actually Take to Hire?

A data engineer role that's been open for 60 days is in the slowest quartile of its peer group. A project manager posting that fills in under two weeks is in the fastest 25%. These benchmarks exist, but most hiring managers operate without them, setting timelines based on gut feel or whatever their last hire happened to take.

We computed fill times across 146 employers in our dataset, breaking them down by role type, employer, and longevity. The numbers give you a baseline for whether your process is on pace, falling behind, or outperforming the market.

The 33-day median

Across all roles and employers, the median time to fill is 33 days. The interquartile range runs from 25 to 38.5 days, meaning half of all roles fill within that window. The full range spans 1 to 70 days, but the extremes tell specific stories about either exceptional brand pull or unusual specialisation.

Roles filling in under 25 days are in the top quartile. Those exceeding 38 days are in the bottom quartile and may signal problems worth investigating: an over-specified role, below-market compensation, or a process that's adding friction without adding signal.

Role type matters more than you'd expect

The gap between the fastest and slowest role types is meaningful. Project managers fill at a 30-day median. ML engineers take 36 days. That six-day gap compounds when you're planning headcount across a team.

Data scientists and data engineers sit in the middle at 32 to 33 days. Analytics engineers are close behind at 34 days. Programme managers, despite sharing most of their skill requirements with project managers, take 37 days, seven days longer than their project management counterparts. The extra time likely reflects the scope of programme management roles. The evaluation process for someone who will own cross-functional delivery is more involved than for someone managing a single workstream.

Job longevity tells the fuller story

Fill time (how long it takes from posting to hire) differs from job longevity (how long the posting stays active). The longevity data captures roles that may still be open, giving a broader view of how long the market keeps positions listed.

Data roles stay open for a median of 44 to 51 days. Management roles are faster at around 31 days. The 75th percentile shows the real divergence: 75% of data roles fill within 60 to 66 days, while 75% of management roles fill within 44 to 45 days.

Data engineers have the longest median longevity among data roles at 51 days. This likely reflects the infrastructure specialisation these roles require. Finding someone who knows your specific stack (the right combination of Airflow, Spark, Snowflake, dbt, and cloud provider) takes longer than finding someone with generalist data skills.

If your data engineer role is still open at day 60, you're at the 75th percentile. That's the point to revisit whether the skill requirements are realistic or whether the compensation is competitive with what the market data shows for the role.

The speed outliers

Some employers consistently fill roles in under two weeks. Block fills at a 9-day median across 50 postings, suggesting systematic hiring efficiency at scale. HubSpot and Abnormal Security fill at 2-day medians, likely reflecting strong employer brands and pre-built candidate pipelines.

On the other end, companies averaging 50 or more days tend to be hiring for specialised roles or running extended evaluation cycles. This is not necessarily a problem. A 55-day fill for a niche ML role may simply reflect the depth of assessment required. But if your standard data engineer role is taking 60 days, the issue is more likely process or positioning than candidate scarcity.

The variance in these numbers is the real story. A six-day gap between project managers (30 days) and ML engineers (36 days) doesn't sound like much. But when you're planning headcount for a cross-functional team that needs a PM, a data engineer, and an ML engineer working together by Q3, those gaps compound. The PM will be onboarded and productive while the ML engineer role is still in second-round interviews. Fill time isn't just an HR metric. It's a sequencing constraint on your roadmap.

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Based on fill time data from 146 employers across 1,525 job postings, tracking data, product, and delivery roles across London, New York, Denver, San Francisco, and Singapore. Fill times reflect posting lifecycle data from company career pages. Data covers November 2025 through February 2026. Full interactive dashboard at richjacobs.me/projects/hiring-market.