Documentation

Canonical jobs table

The application now reads from public.jobs. This table is the clean, canonical version of the old raw import table. V2 suffixes were removed because those taxonomy fields are now the main production fields.

Table

public.jobs

Rows

One row per job posting.

Primary key

id bigint

Identity

Column	Type	Meaning	How to work with it
id	bigint	Stable row identifier for one imported job post.	Use it to open, reference, export, or reconcile one exact job row.

Source job

Column	Type	Meaning	How to work with it
job_title	text	The job title from the original posting.	Search for roles, inspect seniority hints, and sanity-check the function category.
company	text	The hiring company or organization.	Group demand by account, identify repeated buyers, and select target companies.
job_url	text	Original source URL for the job post.	Open the source for manual review or evidence checking.
posted_date	date	Date attached to the job post.	Filter by recency and compare current versus older hiring signals.
location	text	Normalized location bucket for the role.	Filter Munich, remote, Germany, Europe, or other market slices.
employment_type	text	Normalized employment type, such as full time, part time, contract, or internship.	Separate permanent hiring from temporary, student, or contract demand.
applicant_count	integer	Applicant count when the source provided it.	Use as a rough demand/competition signal, not as a precise popularity score.
job_description	text	Full imported job description text.	Use it as the evidence source for manual review, qualification, and future enrichment.

Compensation

Column	Type	Meaning	How to work with it
salary_min	integer	Lower salary bound when available or inferred earlier.	Compare compensation bands. Treat missing values as unknown, not zero.
salary_max	integer	Upper salary bound when available or inferred earlier.	Use with salary_min to estimate midpoint salary by role, sector, or seniority.

Canonical taxonomy

Column	Type	Meaning	How to work with it
seniority_level	text	Canonical seniority bucket: junior, mid_level, or senior.	Segment roles by experience level and compare demand by seniority.
job_function_category	text	Canonical role/function category, focused on what the person does.	Use for role segmentation. Example: software engineering, data/analytics, sales, finance.
industry	text	Canonical employer market category, focused on what the company sells or operates in.	Use for sector analysis. Do not confuse it with job_function_category.
solution_family	text	Broad commercial AI/workflow opportunity family.	Use for high-level offer strategy and dashboard grouping.
solution_cluster	text	Specific sellable workflow or AI automation opportunity.	Use for productized service ideas, outreach angles, and report drilldowns.

Pain signals

Column	Type	Meaning	How to work with it
primary_pain_point	text	Main descriptive business pain inferred from the role.	Read it as narrative evidence. It is useful for sales copy and discovery questions.
primary_pain_point_category	text	Canonical category for the main pain point.	Use for structured filtering and counting repeated problems across jobs.
secondary_pain_point	text	Secondary descriptive pain inferred from the role.	Use to understand nuance before designing an offer or outreach message.
secondary_pain_point_category	text	Canonical category for the secondary pain point.	Use to find combined pain patterns, such as reporting delay plus data quality problems.
pain_frequency_level	text	How often the pain likely appears in the role or process.	Prioritize frequent pains when choosing automation pilots.
pain_intensity_level	text	How severe or costly the pain likely is.	Combine with frequency to decide whether a pain is commercially interesting.

AI and automation

Column	Type	Meaning	How to work with it
ai_applications	jsonb	Descriptive list of possible AI application ideas for the row.	Use as brainstorming material. It is richer text, not a canonical category.
ai_application_category	text	Canonical AI capability category.	Use for counting which AI patterns recur across roles.
automation_opportunities	jsonb	Descriptive list of workflow automation opportunities.	Use to draft pilot scopes and concrete workflow maps.
automation_opportunity_category	text	Canonical automation workflow category.	Use for segmentation and finding repeatable operational automation plays.

Commercial signals

Column	Type	Meaning	How to work with it
complexity_level	text	Estimated implementation complexity for the opportunity.	Prefer lower complexity for first pilots; reserve higher complexity for strategic accounts.
buyer_access_level	text	Estimated ease of reaching or influencing the buyer.	Use to choose outreach channels and prioritize realistic opportunities.
preferred_contact_channel	text	Suggested first outreach channel.	Use for campaign planning and channel-specific messaging.
service_first_level	text	How suitable the opportunity is for a service-led offer before productization.	Use to pick offers that can start as consulting, audits, or pilots.

Common ways to use the table

Find target companies

Group by company, industry, and solution_cluster to find repeated demand by account.

Design offers

Start with solution_family and solution_cluster, then read pain points and automation opportunities for concrete pilot ideas.

Build outreach

Use preferred_contact_channel, buyer_access_level, and the pain fields to shape a specific sales message.

Validate taxonomy

Compare job_description against job_function_category, industry, and solution_cluster when a row looks suspicious.

Prioritize pilots

Combine pain_frequency_level, pain_intensity_level, complexity_level, and service_first_level.

Analyze compensation

Use salary_min and salary_max with seniority_level and job_function_category. Missing salary means unknown, not zero.