Job intelligence
Data Engineer med fokus på machine learning
Friday
Before relying on the listing: Confirm Compensation with the employer or source. See verification questions
Executive opportunity brief
The decision in one screen
A compact evidence-first read of compensation, flexibility, mobility and what still needs verification. No universal quality score is used.
Fast read
Opportunity DNA
The strongest upside: the application route looks comparatively direct. The main trade-off: the pay picture is incomplete.
A financial comparison will need the approved compensation range.
Regular physical-location attendance is part of the advertised arrangement.
The listing does not make the minimum education or experience threshold clear enough yet.
Sponsorship and relocation support are not clear from the current listing.
The visible path is straightforward and no additional mandatory step is stated in the available listing text.
At least one recent enabled source can still renew this vacancy.
This fingerprint summarizes what is clear, constrained or still unanswered in the current listing. It is a reading aid, not an overall job score.
Decision evidence check
4 of 5 core facts stated
This checks whether the listing states the facts that commonly change an employment decision. It measures disclosure completeness, not whether the opportunity is good or bad.
Facts already stated
- Work arrangement
- Location eligibility
- Application closing time
- Employment type
What to verify next
- Compensation: What is the approved compensation range, and what portion is guaranteed base pay rather than variable compensation?
Unknown information is never converted into a negative score. The questions above exist to prevent missing evidence from being mistaken for a favorable term.
Decision levers
What could make or break the fit?
Reasons to look closer
- The application route looks comparatively direct: The primary route points through an employer, an employer-authorized hiring source or an official employment source, and no extra mandatory step is stated in the listing text.
Trade-offs to weigh
- The pay picture is incomplete: No usable salary range is stated, so a meaningful financial comparison is not possible yet.
- Location rules could change the fit: Regular physical-location attendance is part of the advertised arrangement.
Questions worth asking before you invest more time
- Ask for the approved compensation range before comparing this opportunity financially.
- Confirm whether a degree is actually required, preferred, or replaceable with equivalent experience.
- Confirm the actual minimum experience requirement rather than assuming preferred background is mandatory.
- Ask whether visa sponsorship is available before investing significant time in the application.
These questions come from details the listing leaves open. An unanswered point is not treated as a disadvantage.
Opportunity neighborhood
Keep your options open
These live roles sit close enough to this opportunity to make useful comparison points. Look across several employers before deciding what pay, flexibility or access trade-offs you are willing to accept.
Data Engineer i Stockholm
Maxitech · STOCKHOLM, Stockholms län, Sverige
Why it is nearby: Same country · Similar work arrangement · Same employment type
Different angle: Seniority is not explicit
Data Engineer (m/f/d)
ANGEHEUERT GmbH · Frankfurt am Main
Why it is nearby: Similar work arrangement · Same employment type · Recently posted
Different angle: Seniority is not explicit
Machine Learning Engineer III, Routing Cost
Mapbox · Mapbox UK
Why it is nearby: Similar work arrangement · Same employment type · Recently posted
Different angle: Seniority is not explicit
People Tech & Analytics Engineer
Mistral.ai · Paris
Why it is nearby: Similar work arrangement · Same employment type · Recently posted
Different angle: Seniority is not explicit
This neighborhood is drawn from current inventory in the same broad occupation. Similarity is based on explicit listing facts; missing details do not count against a job.
Role market context
How much current choice is visible around this kind of work?
This compares the vacancy with current High Paid Job inventory in the broad Data & AI roles family. It describes visible choice and disclosure, not future demand or career quality.
200 most recent current roles sampled for the disclosure measures below.
Distinct employers in the bounded current sample, not an estimate of all employers hiring in the market.
Current sampled roles stating at least four of the five Core Decision Facts.
Requires a numeric amount, explicit ISO currency and supported pay period.
Listings with explicit enough remote, hybrid or on-site evidence to classify.
Missing deadline evidence remains unknown and is not interpreted as an open-ended application window.
Currencies with explicit pay evidence in the sample: USD (40) · EUR (17) · GBP (5) · CZK (1). Raw amounts across different currencies are not compared.
This describes current visible High Paid Job inventory in the same broad occupation family. Percentages use a bounded recent sample and are not a forecast, a complete labor-market census or an employer-quality measure.
Constraint opportunity lab
What changes the size of the current opportunity pool?
Each line applies one stricter evidence-based constraint to the same bounded current role-family sample. It helps you see the trade-off in visible choice without deciding which trade-off is right for you.
Counts only affirmative or possible sponsorship evidence, or relocation explicitly offered. Unknown is not treated as no.
Uses only roles where a country is known. Roles without country evidence are excluded from this denominator.
A much stricter subset: only listings with explicit worldwide-remote evidence remain.
Shows the subset with enough stated evidence for a more informed first comparison.
Counts roles explicitly classified as worldwide remote, region-restricted remote or remote with restrictions still unclear.
Requires amount, explicit ISO currency and a supported pay period. Missing pay evidence remains unknown.
Uses only roles with a stated employment type.
These are counterfactual filters over a bounded sample of current HPJ inventory in the same broad occupation family. They show how visible choice changes when one evidence-based constraint is applied; they do not tell you which constraint you should choose or predict hiring outcomes. The calculation uses the 200 most recent current roles from 1187 visible roles in this broad family.
Salary transparency context
Is pay disclosure typical for this kind of role?
This listing does not state usable structured compensation. About 32% of the bounded current role sample does.
Compensation counts only when amount, ISO currency and supported pay period are all known.
Pay disclosure is limited in the current role sample.
This employer’s current visible listings disclose pay materially less often than the current same-role sample.
This compares salary-disclosure behavior in current visible listings. It does not compare raw salary amounts across currencies, infer undisclosed pay as zero, or rate employer quality.
Private offer & negotiation lab
Turn an offer into evidence, not guesswork
Enter an annual base offer and, if you want, your own target. HPJ compares only within one explicit currency and never chooses a target for you.
Negotiation evidence available: current same-currency market context, strong decision evidence.
Enter an annual base offer to compare it with explicit same-currency evidence. HPJ will not suggest a target for you.
Review the full package · 10 terms
Mark what is confirmed, what needs clarification and what you want to prioritize. These choices and notes stay in this browser.
The approved base-pay range is not established in the listing.
Variable compensation is mentioned; verify amount, formula, target and eligibility.
Equity or long-term incentive language is mentioned; verify grant type, vesting and valuation basis.
Retirement or pension benefits are mentioned; verify employer contribution and vesting.
Health-benefit language is mentioned; verify coverage, dependants and employee cost.
Paid-leave language is mentioned; verify the exact entitlement and carry-over rules.
A work-arrangement signal is available; verify schedule details before relying on it.
Relocation support not established
Sponsorship not established
A review cycle is mentioned; verify timing and whether compensation can change at that review.
Questions worth taking into the offer conversation
- What is the approved base-pay range for this role?
- Is there a bonus, commission or other variable-pay component, and how is it calculated?
- Does the package include equity or another long-term incentive, and what are the vesting terms?
- What retirement or pension contribution does the employer provide?
- What health coverage is included and what costs remain with the employee?
- How much paid leave is provided and what are the carry-over or blackout rules?
- If relocation is relevant, what expenses, timing and repayment conditions are covered?
- What exact sponsorship or work-authorization conditions apply to this role?
Occupation intelligence
What broader career references connect to this role?
HPJ uses broad comparison families for current-market analysis. External taxonomy results are reference candidates for that broad family, not a declaration that this vacancy has been authoritatively classified into a specific occupation.
European occupation references
This service uses the ESCO classification of the European Commission.
U.S. occupation references
- Data Scientists 15-2051.00
- Computer and Information Research Scientists 15-1221.00
- Social Science Research Assistants 19-4061.00
- Atmospheric and Space Scientists 19-2021.00
- Bioinformatics Scientists 19-1029.01
This site incorporates information from O*NET Web Services by the U.S. Department of Labor, Employment and Training Administration (USDOL/ETA). O*NET® is a trademark of USDOL/ETA.
Career reference research
Skills worth investigating
Official occupational context, paired with exact phrases visible in this vacancy. It is not a skills test or an applicant-fit score.
ESCO · European occupation
data scientist
Usually essential in this ESCO occupation
- statistical modeling techniques
- perform project management
- query languages
- online analytical processing
- develop data processing applications
- data engineering
- manage data collection systems
Context-dependent ESCO skills
- manage ICT data architecture
- SPARQL
- use spreadsheets software
- computational biology
- state estimation
O*NET · U.S. occupation
Data Scientists
O*NET occupation skills
- Mathematics
- Critical Thinking
- Reading Comprehension
- Complex Problem Solving
- Programming
- Active Learning
- Speaking
- Active Listening
Build my private skill preparation brief Your evidence · Practice · Interview questions
Plan how you might demonstrate or investigate occupational skills. These are broad-family reference examples, not confirmed vacancy requirements. Nothing you enter is sent to High Paid Job.
Private to this browser. Up to 20 reference skills; not a qualification assessment or hiring prediction.
Useful next question: Which of these occupational reference skills does the hiring manager actually expect for this specific role, and what evidence would demonstrate them?
These skills belong to one reference occupation selected for HPJ’s broad career family, not a verified classification of this vacancy. Text overlap only means a phrase appears in the available listing text; it does not establish an employer requirement, your proficiency, or a qualification gap. A missing mention is unknown, not a deficit.
Adjacent current roles to investigate
Embedded C++/C Developer Functional Safety & Robotics - 20437
Västerås · Västerås, Västmanlands län, Sverige
same stated country · same seniority band · same employment typeCloud Platform Engineer
Techrytera AB · Göteborg, Västra Götalands län, Sverige
same stated country · same seniority band · same employment typeKubernetes Platform Engineer
Techrytera AB · Göteborg, Västra Götalands län, Sverige
same stated country · same seniority band · same employment typeAdjacent-family roles are exploration prompts from current HPJ inventory. They are not inferred qualification matches and they do not imply a promotion path.
Skill bridge
What does this role connect to?
Large Language Models is one of the signals this listing shares with skills appearing often across current Data & AI roles opportunities.
Skills named in this listing
Shared with the current career sample
- Large Language Models Seen in 37 sampled listings
- Machine Learning Seen in 48 sampled listings
- Python Seen in 88 sampled listings
Combinations also appearing together
- Machine Learning + Python appeared together in 37 sampled listings
- Large Language Models + Python appeared together in 32 sampled listings
- Large Language Models + Machine Learning appeared together in 22 sampled listings
Application edge
- If these skills match your background, prepare one concrete example that shows how you used Large Language Models, Machine Learning, and Python rather than relying on a keyword-only claim.
If the named skills are genuinely transferable in your own experience, the occupation map also points to adjacent current families worth researching: Software engineering roles and Product roles. This is exploration context, not a qualification match.
Skill signals come from explicit text mentions. A mention is not automatically a requirement, and absence from the current top-skill list does not make a skill rare or unimportant. The current career comparison draws on 120 sampled listings; 115 contain a recognized skill signal.
Application Decision Lab
Turn the listing into a preparation plan
Separate what the listing actually states from what you still need to prepare or verify. Your selections and short notes stay in this browser and are never submitted to High Paid Job.
Large Language Models
This skill or technology is explicitly mentioned in the available listing text. A mention is not automatically a mandatory requirement; prepare a concrete example only if it is relevant to your experience.
Machine Learning
This skill or technology is explicitly mentioned in the available listing text. A mention is not automatically a mandatory requirement; prepare a concrete example only if it is relevant to your experience.
Python
This skill or technology is explicitly mentioned in the available listing text. A mention is not automatically a mandatory requirement; prepare a concrete example only if it is relevant to your experience.
Questions worth resolving before you rely on the opportunity
- Compensation: What is the approved compensation range, and what portion is guaranteed base pay rather than variable compensation?
- Work authorization / sponsorship: If sponsorship or work authorization matters to you, what exact immigration or employer-sponsorship conditions apply to this role?
- Application process: What interview, assessment, portfolio, reference or screening stages are part of the hiring process?
Decision analysis
What the listing really tells you
For Data Engineer med fokus på machine learning at Friday, the most useful clues right now are work-location flexibility has usable detail, the visible application effort can be estimated, and the listing can be traced to a current source. Looking at them together makes the trade-offs easier to see.
Work-location flexibility has usable detail
The work-location pattern is “Primarily location-bound”, with Remote Freedom at 18/100. The most important location constraint is regular physical-location attendance is part of the advertised arrangement..
The visible application effort can be estimated
More direct application path. The external application may contain additional steps that are not visible from the listing itself.
The listing can be traced to a current source
1 recent enabled source can currently renew this vacancy across 1 source classes. That improves traceability, although it does not verify the employer itself.
How this analysis is assembled
The brief pulls together the parts of the vacancy that can be compared reliably today. Gaps in the listing remain visible instead of being filled with assumptions.
Analysis evidence coverage
How much analysis can the current evidence support?
Comparable salary data is not available for this job yet.
Measures observable location flexibility and explicit restrictions; it does not determine whether remote work is personally desirable.
Missing compensation is not treated as negative evidence; this dimension simply remains unavailable.
The listing was posted about 1 days ago.
The vacancy has recent enabled provenance through a comparatively direct source lane. This strengthens traceability, not employer verification.
There is no universal opportunity score because pay, flexibility and access matter differently to different people. Compare Jobs lets you weight the evidence around your own priorities.
Decision checklist
What is supported, uncertain or worth checking?
This checklist separates supported strengths, visible cautions and missing evidence. It is not an overall recommendation or guarantee that the job is suitable for you.
Supported strengths
- Recent provenance includes a comparatively direct source lane, improving traceability.
- The current provenance provides a comparatively direct application route.
Visible cautions
- Regular physical-location attendance is part of the advertised arrangement.
Still unknown
- There is not enough comparable salary evidence to establish this job’s current pay position.
- Visa sponsorship is not established by the available listing data.
Pay vs. barriers
Opportunity Efficiency not assessed
The current pay position is not reliable enough to weigh compensation against the stated barriers.
1 explicit Remote Freedom restriction signal is present and is shown separately from qualification requirements.
- Current comparable pay position is unavailable.
- Education requirement is not established.
- Required prior-experience level is not established.
This section weighs pay context against clearly stated barriers without collapsing unlike requirements into one universal score. Degree, experience, licensing, clearance and location limits remain separate, and missing qualification details stay unknown.
Beyond the headline
Not enough evidence for a hidden-value finding
Potential hidden value appears only when several distinct non-pay advantages are actually supported. Missing requirements or benefits are never counted as positives.
Hidden Value is an experimental evidence summary, not an investment-style score or guarantee of employability. It only uses explicit current listing evidence and does not infer that an unmentioned qualification is unnecessary.
Compensation intelligence
How competitive is this pay?
Comparable salary data is not available for this job yet.
Why this analysis?
- Comparable salary data is not available for this job yet.
- Salary confidence: Not available. No disclosed salary can currently be normalized for comparison. Missing compensation is not treated as low-quality salary evidence.
- Range quality: No disclosed range.
Employer compensation communication
Pay transparency at Friday
Limited current pay transparency
This score uses the employer’s current visible listings and combines how often pay is disclosed with how useful those ranges are. Bonus, equity and benefit mentions are shown separately and do not raise the score.
This pattern comes from the employer’s current visible vacancies. It describes hiring and pay transparency in those listings, not employee satisfaction or a permanent judgement of the employer.
Current employer intelligence
Friday — current opportunity snapshot
Employer Evidence Profile
How often this employer’s current visible sampled vacancies state decision-critical information. These percentages describe evidence coverage, not employer quality.
Current sampled listings with advertised compensation evidence.
Current sampled listings whose work arrangement is clearer than unknown.
Current sampled listings with a supported country signal.
Current sampled listings with an explicit closing time.
Current sampled listings with a usable posting date.
Current sampled listings that explicitly indicate confirmed, possible or unavailable sponsorship evidence.
Current sampled listings with an explicit relocation signal.
Low coverage means the current visible listings state the information less often. It does not prove that the employer lacks the policy, benefit or support.
Common current occupation families: Data & AI (3)
Visible seniority mix: Early-career (2)
Current employment-type mix: Full time (14)
Most visible hiring countries in this sample: Sweden (14)
Other current roles from this employer
Elkonstruktör inom Automation till teknikbolag i Stockholm
Stockholm, Stockholms län, Sverige
Seniority not clearly specified · On-site / hybridJunior Projektadministratör till Energibolag
Kalmar, Kalmar län, Sverige
Early-career · On-site / hybridProjektledare inom fastighetsteknik
Stockholm, Västmanlands län, Sverige
Seniority not clearly specified · On-site / hybridJunior mjukvaruutvecklare C++/Python - avancerade sensorsystem
Stockholm, Stockholms län, Sverige
Early-career · On-site / hybridThese are other current vacancies visible from the same employer, not endorsements or evidence of future hiring plans.
There are not yet five current disclosed salaries in one currency, so an employer pay median would be too thin to be useful.
Questions worth asking this employer
- What compensation range applies to this role, and which parts of total compensation are fixed versus variable?
- If work authorization matters to you, what sponsorship policy applies to this specific role and location?
- If a move is required, is relocation assistance available for this position?
These questions are prompted by missing or thin evidence in the employer’s current visible vacancies. Missing evidence is not treated as a negative judgement.
This employer snapshot uses the employer’s current visible listings. Remote and sponsorship rates use only vacancies with explicit evidence, while missing information stays unknown. Salary medians are shown separately by currency and need at least five disclosed salaries. Zero confirmed sponsorship means none was explicitly found in this sample, not that sponsorship is unavailable.
This snapshot describes the employer’s current visible vacancies—things such as pay disclosure and work arrangement. It is not an employee-satisfaction or hiring-quality rating, and different currencies stay separate.
Work-location intelligence
Remote Freedom
Primarily location-bound
Why this score?
- The listing is on-site or hybrid rather than location independent.
Location restrictions detected
- Regular physical-location attendance is part of the advertised arrangement.
Remote Freedom measures observable location flexibility only. Unknown work-location information remains unscored. Timezone wording is penalized only when it appears restrictive; general collaboration across time zones is not treated as a restriction. A score does not guarantee legal work eligibility, payroll availability or visa sponsorship.
International applicant intelligence
Visa & relocation signals
Sponsorship status is shown only when the listing provides clear enough wording. Employer history or headquarters location is not used to guess an answer.
The available listing details do not contain a clear enough sponsorship statement.
The available listing details do not contain a clear enough relocation statement.
Tradeoff check
What’s the catch?
No obvious trade-off appears in the available job details. That does not mean the role has no drawbacks, so the original listing and employment terms are still worth reading carefully.
Application-path intelligence
What can we see about the application process?
More direct application path
The primary route is a direct-employer, employer-authorized hiring or official government source. That can reduce intermediary steps, although the external form may still be lengthy.
Current destination: pnty-apply.ponty-system.se · HTTPS
No additional required application step is clear in the available listing text. This does not mean the external application is one-click or friction-free.
Application effort is estimated from visible listing and source details. Optional steps are kept separate from mandatory ones, and the external form may contain additional requirements that are not visible here.
Application safety context
Application safety check
Basic application-safety context available
The current application destination has a basic transport-safety signal such as HTTPS. This does not verify the employer, vacancy, recruiter or application process.
Evidence checked: Source provenance · Application destination · Listing wording
Observable transport-safety signals
- The available application destination uses HTTPS.
Before you share money or sensitive information
- Confirm the vacancy on the employer’s official careers site when practical, especially if a recruiter contacts you outside the listed application path.
- Check that recruiter email domains and application destinations are consistent with the employer or an identifiable recruiting provider.
- Do not pay application, processing, training or equipment fees merely to be considered for a job.
- Do not send cryptocurrency, gift cards, online-banking credentials or PINs as part of an application.
- Treat unusually fast offers or interviews conducted only through informal messaging channels as reasons to verify the employer independently.
This is a limited safety screen, not employer verification and not a fraud determination. Never send money, cryptocurrency, passwords or sensitive banking credentials merely because a listing appears on a job platform.
Original listing details
The section below comes from the listing source. The decision sections above use the job’s structured pay, location, qualification, mobility and application details to give you additional context.
Här får du möjlighet att utveckla datalösningar som används i praktiken - från att bygga robusta datapipelines och automatisera dataflöden till att utforska hur machine learning och LLMs kan användas för att skapa värde ur komplexa datamängder.
OM TJÄNSTEN
I rollen som Data Engineer blir du en del av en teknisk miljö där stora mängder data från både interna och externa källor behöver samlas in, bearbetas och göras tillgängliga för analys och vidare användning.
Du arbetar genom hela dataflödet, från inhämtning och transformation till kvalitetssäkring, automatisering och integration med analys- och BI-lösningar. En viktig del av arbetet handlar om att utveckla lösningar som inte bara fungerar tekniskt, utan också är tillförlitliga, skalbara och möjliga att vidareutveckla över tid.
Rollen innehåller även inslag av AI och machine learning, där du får möjlighet att utvärdera och implementera tekniker för exempelvis klassificering, berikning och analys av data. Det kan innebära arbete med såväl traditionella ML-metoder som LLM-baserade lösningar, beroende på problemställning och behov.
Du kommer in i en befintlig teknisk miljö med verktyg som Python, Pandas, PySpark och Apache Airflow. Arbetet sker i nära dialog med både tekniska kollegor och verksamhetsnära intressenter, där du får vara med och omsätta behov till konkreta tekniska lösningar.
ARBETSUPPGIFTER
- Utveckla, vidareutveckla och underhålla datapipelines för inhämtning, bearbetning och transformation av data.
- Utveckla Python-baserade lösningar för databehandling, analys och automatisering.
- Arbeta med datakvalitet, validering och strukturering av data från olika interna och externa källor.
- Implementera och vidareutveckla automatiserade arbetsflöden med exempelvis Apache Airflow.
- Utforska och tillämpa machine learning och LLM-baserade tekniker för relevanta användningsområden.
- Integrera bearbetad data med nedströms analysverktyg och BI-plattformar.
- Felsöka, optimera och kvalitetssäkra dataflöden med fokus på prestanda, stabilitet och långsiktig förvaltning.
- Dokumentera lösningar och bidra till kunskapsöverföring för att säkerställa att de kan vidareutvecklas och underhållas över tid.
VI SÖKER DIG SOM
- Har högskole - eller civilingenjörsutbildning inom relevant område.
- Har 1-3 års arbetslivserfarenhet i en liknande roll
- Har stark teknisk grund inom Pyhton och data engineering
- Har god förståelse för machine learning och LLM samt praktisk erfarenhet av att tillämpa AI/ML på datarelaterade problem.
Vi tror att du är en person som tycker om att förstå hur saker fungerar på djupet och som gärna tar dig an komplexa problem där lösningen inte alltid är given från början. Du arbetar strukturerat och självständigt, tar ansvar för dina leveranser och har ett naturligt fokus på kodkvalitet och långsiktigt hållbara lösningar. Samtidigt är du pragmatisk och kan avgöra när en lösning är tillräckligt bra för att skapa värde och när det krävs ytterligare teknisk fördjupning. Eftersom rollen innebär samarbete med personer med olika teknisk bakgrund är det också viktigt att du kan diskutera tekniska vägval, förstå verksamhetens behov och förklara dina lösningar på ett tydligt sätt.
OM ANSTÄLLNINGEN:
Detta är ett konsultuppdrag på initialt 6 månader där du kommer vara anställd hos Friday.
Denna tjänst omfattas av säkerhetsskydd. Säkerhetsprövning med registerkontroll kommer att genomföras före anställning i enlighet med säkerhetsskyddslagen. För vissa roller kan krav på svenskt medborgarskap förekomma.
ÖVRIG INFO:
- Omfattning: Heltid
- Start: Omgående
- Placering: Göteborg
- Lön: Marknadsmässig
- Rekryteringsansvarig:Hanna Andersson
Ansök genom att klicka på länken nedan. Kom ihåg att vara snabb med din ansökan då vi gör löpande urval av kandidater och att annonsen kan stängs ner innan tjänsten är tillsatt om vi gått över till urvals och intervjufasen.
OM FRIDAY:
På Friday hjälper vi Tech-talanger att hitta roller där de trivs, utvecklas och gör skillnad. Genom att lyssna på vad just du vill och drivs av, matchar vi dig med företag och möjligheter som får dig att se fram emot att gå till jobbet. Vi värderar ambition och potential högt och arbetar aktivt för en fördomsfri rekrytering, där alla ges samma chans att lyckas. Vi finns i Stockholm, Göteborg, Malmö, Linköping och Örebro. Varje år genomför vi över 5 000 karriärmöten och matchar kandidater med allt från globala företag till innovativa startups.
Listing provenance
Where did this job come from?
See where this vacancy came from and whether more than one current source can still support it. Source names help with traceability and attribution; they are not endorsements or guarantees.
The vacancy has recent enabled provenance through a comparatively direct source lane. This strengthens traceability, not employer verification.
- Continuity
- One recent renewable source
- Source-class independence
- One renewable source class
- Last source observation
- Observed today
This records when High Paid Job last observed the vacancy in source evidence. It confirms source presence, not that the employer edited the vacancy at that time.
- 1 enabled source recently observed this vacancy.
- At least one recent enabled source is a direct-employer, employer-authorized or official public source.
Source confidence is about traceability and continuity. It is separate from application safety and should not be read as an employer reputation rating.
This vacancy is supplied through an official public employment service. The source identifies where the listing was published; it is not an employer endorsement.
Listing source: Arbetsförmedlingen. Destination: pnty-apply.ponty-system.se. The application opens in a new tab so you can keep this report open.