company-growth-trends

company-growth-trends

Two complementary data sources accessed via separate tools:

What you need	Tool to use	Source
Total company headcount over time + (optional) employee roster with prior-company	get_company_headcount	ONFIRE.PEOPLE_GRAND_EXPERIENCES (tenure-based) + ONFIRE.PEOPLE_GRAND (profile enrichment). Tool-managed; never exposed via query_onfire.
Technology / persona growth (e.g. Sentinex, data engineer)	query_onfire → ONFIRE.GROWTH_INSIGHT_MONTHLY	Pre-computed monthly % change

Rule: Never use query_onfire for total headcount questions. Always use get_company_headcount.

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When to use this

• "Is Meridian Bank's Sentinex footprint growing?" → query_onfire (insight)
• "Compare EDR adoption to total headcount at these 5 accounts" → both tools, then compare
• "Which of our target accounts are shrinking overall?" → get_company_headcount
• "Is the data engineering persona expanding at Frostbyte?" → query_onfire (insight)
• "Who joined Northwind in the last 12 months and where did they come from?" → get_company_headcount (roster is always returned)
• "Who left Northwind in the last quarter and where did they go?" → get_company_headcount (roster includes leavers + next-company)
• Any month-over-month growth / trend question for companies

Skip this for:

• Single-month roster of current employees by keyword → use employee-footprint
• Open requisitions → use hiring-signals

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get_company_headcount — total headcount + employee roster

Headcount is derived from tenure rows in ONFIRE.PEOPLE_GRAND_EXPERIENCES: for each month M in the window, the tool counts distinct people whose tenure covers M (START_DATE <= last-day-of-M AND END_DATE IS NULL OR END_DATE >= first-day-of-M). MoM growth % is computed from those counts.

The roster is always returned alongside the monthly counts (no opt-in flag). One row per (person × stint) where any of these hold:

• start_date falls in the lookback window → joiner
• end_date IS NULL → still active
• end_date falls in the lookback window → leaver (covers long-tenured departures whose start is outside the window)

Each row is joined to ONFIRE.PEOPLE_GRAND for profile fields (name, country, region, current title) and self-joined to ONFIRE.PEOPLE_GRAND_EXPERIENCES for the person's immediately-prior AND immediately-next company. next_* is only populated for stints that ended (end_date IS NOT NULL). Both tables are tool-managed — query_onfire cannot read them.

Parameters

Parameter	Type	Notes
company_linkedin_urls	List[str]	Required. Up to 50 URLs. Any variant accepted; normalized to linkedin.com/company/<slug>.
months	int	Optional. Months of history (default 13, max 36). Also the roster cutoff — joiners with start_date in the window, leavers with end_date in the window, plus all still-active employees.

Response shape

Both keys are always present.

{
  "headcount": {
    "dataset":     {dataset handle},
    "total_count": N,
    "preview_rows": [first 20 rows]
  },
  "employees": {
    "dataset":     {dataset handle},
    "total_count": M,
    "preview_rows": [first 20 rows]
  }
}

Headcount rows (most-recent first)

Column	Notes
company_linkedin_url	Normalized to linkedin.com/company/...
time_period	First day of the month (e.g. 2026-01-01)
employee_count	Distinct people whose tenure covers that month
growth_pct	MoM % change (NULL for the oldest row in the window)

The first row per company is the current month's count.

Employee rows (one per stint)

Column	Source	Notes
company_linkedin_url	ONFIRE.PEOPLE_GRAND_EXPERIENCES	Target company
person_linkedin_url, person_linkedin_id	ONFIRE.PEOPLE_GRAND_EXPERIENCES	The employee
is_primary	ONFIRE.PEOPLE_GRAND_EXPERIENCES	LinkedIn's "current primary role" flag
start_date, end_date	ONFIRE.PEOPLE_GRAND_EXPERIENCES	YYYY-MM strings (NULL end = still there)
title_name, title_role, title_sub_role, title_levels	ONFIRE.PEOPLE_GRAND_EXPERIENCES	Title classification at the company
full_name, headline	ONFIRE.PEOPLE_GRAND	Latest snapshot
location_country, location_region, location_continent, location_name	ONFIRE.PEOPLE_GRAND	Person's location
current_job_title, current_company_name, current_company_linkedin_url	ONFIRE.PEOPLE_GRAND	What they're doing now (may differ from the target stint if end_date IS NOT NULL)
prior_company_name, prior_company_linkedin_url, prior_title	self-join on ONFIRE.PEOPLE_GRAND_EXPERIENCES	Where they came from before the target stint
prior_start_date, prior_end_date	self-join on ONFIRE.PEOPLE_GRAND_EXPERIENCES	Prior stint's window
next_company_name, next_company_linkedin_url, next_title	self-join on ONFIRE.PEOPLE_GRAND_EXPERIENCES	Where they went after the target stint. NULL when end_date IS NULL (still active)
next_start_date, next_end_date	self-join on ONFIRE.PEOPLE_GRAND_EXPERIENCES	Next stint's window

Deriving joiners / leavers from the roster

The roster is one source of truth for both directions:

• Joiners — WHERE start_date IN window AND end_date IS NULL (or include short-stint leavers with end_date IN window if you want all arrivals)
• Joiner origins — prior_company_name is already attached
• Leavers — WHERE end_date IN window
• Leaver destinations — next_company_name is already attached

Example calls

# Default — monthly series + full roster (joiners, leavers, still-active)
get_company_headcount(
    company_linkedin_urls=["linkedin.com/company/packmint"],
    months=12,
)

Example interpretation

• employee_count: 533, growth_pct: 0.2 → 533 active employees this month, +0.2% MoM
• growth_pct: null → oldest month in the window (no prior month to diff against)
• growth_pct: -1.5 → headcount contracted 1.5% MoM
• An employee row with end_date IS NULL is currently at the company
• prior_company_name = "Globex" → the person was at Globex right before this stint; useful for joiner-origin / talent-flow analysis
• next_company_name = "Frostbyte" → after leaving the target company they went to Frostbyte; useful for leaver-destination analysis (only set when end_date IS NOT NULL)

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query_onfire → GROWTH_INSIGHT_MONTHLY — technology / persona growth

Table structure

Column	Type	Notes
company_linkedin_url	TEXT	Company identifier
company_website	TEXT	Company website
insight_name	TEXT	Technology/persona slug, always prefixed growth- (e.g. growth-EDR, growth-data_analyst)
time_period	DATE	First day of the month
str_value	TEXT	Growth % as a string (e.g. "12.5" = +12.5%). NULL on the first data point.
deleted_at	TEXT	Always filter IS NULL

Key derived value: TRY_CAST(str_value AS FLOAT) to get numeric %.

Required WHERE clause

WHERE deleted_at IS NULL
  AND str_value IS NOT NULL   -- excludes first-month baseline rows
  AND company_linkedin_url IN (...)

SQL templates

Tech insight growth for one company

SELECT
    company_linkedin_url,
    insight_name,
    time_period,
    TRY_CAST(str_value AS FLOAT) AS growth_pct
FROM ONFIRE.GROWTH_INSIGHT_MONTHLY
WHERE deleted_at IS NULL
  AND str_value IS NOT NULL
  AND company_linkedin_url ILIKE '%/company/meridian-bank%'
  AND LOWER(insight_name) = 'growth-sentinex'
ORDER BY time_period DESC
LIMIT 13

All tech insights for a company (last 3 months)

SELECT
    insight_name,
    time_period,
    TRY_CAST(str_value AS FLOAT) AS growth_pct
FROM ONFIRE.GROWTH_INSIGHT_MONTHLY
WHERE deleted_at IS NULL
  AND str_value IS NOT NULL
  AND company_linkedin_url ILIKE '%/company/frostbyte%'
  AND time_period >= DATEADD(month, -3, CURRENT_DATE())
ORDER BY insight_name, time_period DESC
LIMIT 200

Multiple companies — which insight is growing fastest?

SELECT
    company_linkedin_url,
    insight_name,
    time_period,
    TRY_CAST(str_value AS FLOAT) AS growth_pct
FROM ONFIRE.GROWTH_INSIGHT_MONTHLY
WHERE deleted_at IS NULL
  AND str_value IS NOT NULL
  AND company_linkedin_url IN (
      'linkedin.com/company/northwind',
      'linkedin.com/company/sendline',
      'linkedin.com/company/pathwatch'
  )
  AND insight_name ILIKE 'growth-edr%'
  AND time_period = (
      SELECT MAX(time_period)
      FROM ONFIRE.GROWTH_INSIGHT_MONTHLY
      WHERE deleted_at IS NULL
  )
ORDER BY growth_pct DESC NULLS LAST
LIMIT 50

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Comparing total headcount vs tech growth

Call both tools and merge the results in your response.

Step 1 — get total headcount:

get_company_headcount(
    company_linkedin_urls=["linkedin.com/company/skywall-networks"],
    months=13,
)

Step 2 — get tech insight growth via query_onfire:

SELECT
    time_period,
    TRY_CAST(str_value AS FLOAT) AS growth_pct
FROM ONFIRE.GROWTH_INSIGHT_MONTHLY
WHERE deleted_at IS NULL
  AND str_value IS NOT NULL
  AND company_linkedin_url ILIKE '%/company/skywall-networks%'
  AND insight_name ILIKE 'growth-edr%'
ORDER BY time_period DESC
LIMIT 13

Step 3 — narrate the comparison aligned by time_period.

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Interpreting results

• Positive growth_pct → company / tech is growing that month.
• Negative growth_pct → contracting.
• growth_pct IS NULL → first data point in the series. Filter out for charting.
• insight_name always has the growth- prefix in stored values. Use ILIKE 'growth-%edr%' for fuzzy matching.

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Output handling

Both tools return a dataset handle + preview_rows (first 20).

1. Narrate the trend: "Skywall Networks' total headcount grew +3.2% MoM in April 2025, while Sentinex persona adoption grew +8.5% — outpacing overall hiring."
2. For time-series questions, show a table of time_period → growth_pct.
3. Offer download_dataset for the full CSV.
4. Follow-up slices → use query_datasets against the existing dataset_id.

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Common pitfalls

• Using query_onfire for total headcount — always use get_company_headcount instead.
• Treating next_company_name as "current employer" — it's the next stint after this one. For still-active people (end_date IS NULL) it's NULL. Use current_company_name for "where they work now".
• str_value is TEXT in GROWTH_INSIGHT_MONTHLY — always use TRY_CAST(str_value AS FLOAT) for arithmetic.
• Forgetting str_value IS NOT NULL — NULL rows are baseline-only and distort averages.
• insight_name must have the growth- prefix — use ILIKE 'growth-%edr%' for fuzzy matching.
• growth_pct NULL on oldest row — the LAG window has no prior month. Filter it out before computing averages.
• start_date / end_date in the employee roster are YYYY-MM strings, not full dates. Treat them as month granularity.