Job intelligence
Data Engineer till JM, Solna
JM AB
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
There is a useful advantage here: the application route looks comparatively direct. Still, the pay picture is incomplete could change the fit.
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
- Advertised location
- 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?
Advertised location is not proof of work authorization, sponsorship, or legal eligibility. Unknown information is not a negative score; verify those questions separately.
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 Scientist
Hawk · Munich
Why it is nearby: Similar work arrangement · Same employment type · Recently posted
Different angle: Seniority is not explicit
AI Engineer
Juro · London (UK)
Why it is nearby: Similar work arrangement · Same employment type · Recently posted
Different angle: Seniority is not explicit
Machine Learning Engineer – Practice IA MARGO
MARGO · Paris
Why it is nearby: Similar work arrangement · Same employment type · Recently posted
Different angle: Seniority is not explicit
Senior AI Engineer, Location AI
Mapbox · Mapbox Germany
Why it is nearby: Similar work arrangement · Same employment type · Recently posted
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 (39) · EUR (17) · GBP (6) · CAD (2). 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.
Requires amount, explicit ISO currency and a supported pay period. Missing pay evidence remains unknown.
Counts roles explicitly classified as worldwide remote, region-restricted remote or remote with restrictions still unclear.
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 1201 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 33% 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.
Employer-level comparison is withheld until enough current employer listings are available.
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
Android Developer (f/m/d)
Zeal Network · Hamburg
same seniority band · same employment type · similar work-arrangement evidenceFrontend Engineer
Bjak · United Kingdom
same seniority band · same employment type · similar work-arrangement evidenceFounding Product Manager (German-speaking)
Bowatt · Munich
same seniority band · same employment type · similar work-arrangement evidenceAdjacent-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?
Azure 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
- Azure Seen in 17 sampled listings
- Snowflake Seen in 25 sampled listings
Additional signals in this listing: Power BI · SAP
Application edge
- If these skills match your background, prepare one concrete example that shows how you used Azure, Power BI, and SAP 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.
Azure
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.
Power BI
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.
SAP
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.
Snowflake
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
The headline alone does not tell the whole story for Data Engineer till JM, Solna at JM AB. The clearest decision context comes from the location rules are clear enough to judge, the visible application effort can be estimated, and the listing can be traced to a current source.
The location rules are clear enough to judge
Location flexibility can be assessed here: Primarily location-bound, with a Remote Freedom reading of 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 source posting date is today.
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 JM AB
Only 2 current listings from this employer are indexed. At least three are required before employer-level pay transparency is summarized.
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
JM AB — current opportunity snapshot
Only 2 current listings from this employer are visible. At least three are needed before an employer pattern is shown.
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: apply.recman.page · 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.
JM är Nordens ledande projektutvecklare av bostäder och bostadsområden. Verksamheten är fokuserad på nyproduktion av bostäder i attraktiva lägen med tyngdpunkt på expansiva storstadsområden och universitetsorter i Sverige, Norge och Finland. Vi arbetar också med projektutveckling av kommersiella lokaler samt entreprenadverksamhet, huvudsakligen i Storstockholmsområdet.JM ska i all verksamhet främja ett långsiktigt hållbarhetsarbete. Vi omsätter cirka 10 miljarder kronor och har cirka 1 800 medarbetare. JM AB är ett publikt bolag noterat på NASDAQ Stockholm, segmentet Large Cap.På JM är du med och driver utvecklingen framåt och tillsammans bygger vi framtidens samhällen.
Vill du ta en nyckelroll i att bygga upp en modern dataplattform från grunden – och samtidigt utvecklas mot en mer arkitektonisk roll över tid? Då kan detta vara rätt nästa steg för dig.
Om tjänsten
Som Data Engineer på JM kliver du in i ett läge där vi står inför en omfattande förändringsresa inom dataområdet. Vi går från en traditionell on‑premise-lösning till en modern molnbaserad dataplattform i Snowflake, med målet att möjliggöra ett mer datadrivet arbetssätt i hela organisationen.
Det här är inte en roll där du förvaltar något färdigt, utan du blir en av nyckelpersonerna i att bygga upp och forma vår dataplattform, våra arbetssätt och vår struktur framåt. Rollen är initialt hands-on med fokus på migrering och uppbyggnad av plattformen, samtidigt som du har stort inflytande över tekniska val, lösningar och hur vi långsiktigt arbetar med data.
I takt med att plattformen mognar finns en tydlig möjlighet att växa i rollen och successivt ta ett större helhetsansvar, med en naturlig utveckling mot en mer arkitektur- och designorienterad roll. Du får möjlighet att vara med och bygga upp en modern dataplattform från grunden. Du får stort inflytande över teknikval, struktur och arbetssätt och blir en nyckelperson i en affärskritisk transformation.
I rollen har du ansvar för att driva utveckling och förvaltning inom dina områden för hela koncernen, som omfattar Sverige, Norge och Finland. Du rapporterar till Lars Lundin, Gruppchef Integration & Analys, och arbetar nära både verksamheten och övriga IT-team i en miljö som präglas av samarbete, förtroende och eget ansvar.
Exempel på arbetsuppgifter som ingår i rollen:
- Driva och genomföra migrering från befintligt Data Warehouse till Snowflake
- Designa, bygga och optimera datapipelines (ETL/ELT)
- Bygga och vidareutveckla den molnbaserade dataplattformen
- Arbeta med datamodellering för analys och beslutsstöd
- Säkerställa prestanda, skalbarhet och kostnadseffektivitet
- Etablera data governance, inklusive datakvalitet, lineage och affärsregler
- Arbeta med automatisering, testning och monitorering
- Hantera CI/CD-flöden i Azure DevOps
- Arbeta nära verksamheten för att förstå databehov och skapa affärsvärde
- Stötta Data Analyst i Power BI vid behov
Bidra till tekniska riktlinjer, arkitektur och best practice
Din profil
För att lyckas i rollen söker vi dig som:
- Har minst 5 års erfarenhet inom Data Engineering
- Har mycket goda kunskaper i SQL
- Har erfarenhet av datamodellering (t.ex. Kimball, star schema)
- Har arbetat med datapipelines (ETL/ELT) i produktion
- Har erfarenhet av Snowflake
- Har erfarenhet av dbt eller liknande verktyg
- Har arbetat i molnmiljö, exempelvis Azure
- Har erfarenhet av Python
- Har arbetat med CI/CD och DevOps
- Har god förståelse för dataarkitektur och data governance
Meriterande: Erfarenhet av datakataloger, Power BI/DAX, större datamigreringar, finansiell data eller SAP samt bakgrund från fastighets- eller byggbranschen. Akademisk utbildning på universitetsnivå inom relevant område ses som meriterande.
Rollen innebär att du kommunicerar obehindrat på både svenska och engelska, i tal och skrift.
Vi lägger stor vikt vid dina personliga egenskaper. Du är självgående, strukturerad och lösningsorienterad med ett starkt eget driv. Du trivs i en miljö som är under utveckling, där du får ta ansvar, tänka framåt och påverka hur saker ska göras. Samtidigt har du en god kommunikativ förmåga och uppskattar att arbeta nära både teknik och verksamhet.
Vi erbjuder
Vi erbjuder dig ett arbete hos Nordens ledande bostadsutvecklare. Med god lönsamhet har vi varit drivande i att utveckla bostadsmarknaden sedan 1945. En nyckel till framgång är vårt strukturerade arbetssätt som vi etablerat och utvecklat sedan många år tillbaka. Här behöver du inte uppfinna hjulet, men du blir en viktig del i att ständigt utveckla det. Här finns förutsättningarna för dig att växa både i din yrkesroll och i din kompetens.
Hos oss är du med och påverkar hur framtidens samhällen utformas. Här värdesätter vi din kompetens och ditt bidrag och vi arbetar långsiktigt för att främja jämställdhet och mångfald. Vi bidrar till ett samhälle där människors lika värde står i centrum och där olikheter ses som en styrka. Med människan i fokus, ett inbyggt långsiktigt hållbarhetsarbete och genom ständig utveckling skapar vi boenden och livsmiljöer som lägger grunden till ett bättre liv.
Här på JM är du del av ett team, där din kompetens och ditt bidrag är viktigt för att vi ska fortsätta leverera och bli bättre.
Välkommen med din ansökan!
Ansökan
I denna rekrytering samarbetar JM med Level Recruitment. Vänligen sök tjänsten genom att klicka på "ansök"-knappen här intill. Vid frågor vänligen kontakta Rekryteringskonsult Stina Koskijev på 08-120 50 421, eller stina.koskijev@levelrecruitment.se (vänligen observera att inga ansökningar tas emot via e-post).
När du söker tjänsten får du gärna vara utförlig i ditt CV och tydligt beskriva vad du har arbetat med, vilka tekniker du behärskar samt särskilt lyfta de kompetenser du vill att vi uppmärksammar.
Urvalet av ansökningar sker löpande. Urvalsarbetet påbörjas vecka 33 med hänsyn till semestrar.
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 very recently
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: apply.recman.page. The application opens in a new tab so you can keep this report open.