{"id":78069,"date":"2020-04-16T01:42:42","date_gmt":"2020-04-15T23:42:42","guid":{"rendered":"https:\/\/prohoster.info\/blog\/administrirovanie\/data-engineer-i-data-scientist-chto-umeyut-i-skolko-zarabatyvayut"},"modified":"2020-04-16T01:42:42","modified_gmt":"2020-04-15T23:42:42","slug":"data-engineer-i-data-scientist-chto-umeyut-i-skolko-zarabatyvayut","status":"publish","type":"post","link":"https:\/\/prohoster.info\/hr\/blog\/administrirovanie\/data-engineer-i-data-scientist-chto-umeyut-i-skolko-zarabatyvayut","title":{"rendered":"Data Engineer i Data Scientist: \u0161to rade i koliko zara\u0111uju","gt_translate_keys":[{"key":"rendered","format":"text"}]},"content":{"rendered":"<p>Zajedno s Elenom Gerasimovom, voditeljicom fakulteta \u201e<noindex><a rel=\"nofollow\" href=\"https:\/\/netology.ru\/navigation\/?direction=data-science?utm_source=habr&amp;utm_medium=externalblog&amp;utm_campaign=bds_all_ou&amp;utm_content=14042020_dataengscientist\">Data Science i analitika<\/a><\/noindex>\u201c u Netologiji nastavljamo istra\u017eivati kako se me\u0111usobno povezuju i \u010dime se razlikuju Data Scientist i Data Engineer.<\/p>\n<p>U prvom dijelu govorili smo <noindex><a rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/company\/netologyru\/blog\/496082\/\">o osnovnim razlikama izme\u0111u Data Scientista i Data Engineera<\/a><\/noindex>. <\/p>\n<p>U ovom materijalu razgovarat \u0107emo o tome kakva znanja i vje\u0161tine trebaju imati stru\u010dnjaci, kakvo obrazovanje poslodavci cijene, kako prolaze razgovori za posao, te koliko zara\u0111uju data in\u017eenjeri i data scienisti.\u00a0<noindex><a rel=\"nofollow\" name=\"habracut\"><\/a><\/noindex><\/p>\n<h2>\u0160to trebaju znati scientisti i in\u017eenjeri<\/h2>\n<p>\nStrukovno obrazovanje za oba stru\u010dnjaka \u2013 Computer Science.<\/p>\n<p><img decoding=\"async\" alt=\"Data Engineer i Data Scientist: \u0161to rade i koliko zara\u0111uju\" src=\"\/wp-content\/uploads\/2020\/04\/14e804895cb4eac665c2b8558a5911ba.jpg\" style=\"display:block;margin: 0 auto;\" \/><br \/>\n<br \/>\nSvaki stru\u010dnjak za podatke \u2013 data scientist ili analiti\u010dar \u2013 mora znati dokazati ispravnost svojih zaklju\u010daka. Za to je neophodno poznavanje <b>statistike i povezane osnovne matematike<\/b>.<\/p>\n<p>Strojno u\u010denje i alati za analizu podataka su neizostavni u modernom svijetu. Ako uobi\u010dajeni alati nisu dostupni, potrebno je imati vje\u0161tine <b>brzog u\u010denja novih alata, stvaranja jednostavnih skripti za automatizaciju zadataka<\/b>.<\/p>\n<p>Va\u017eno je naglasiti da stru\u010dnjak za obradu podataka mora u\u010dinkovito prenijeti rezultate analize. U tome \u0107e mu pomo\u0107i <b>vizualizacija podataka<\/b> ili rezultata provedenih istra\u017eivanja i testiranja hipoteza. Stru\u010dnjaci trebaju znati kreirati dijagrame i grafikone, koristiti alate vizualizacije, razumjeti i obja\u0161njavati podatke iz nadzornih plo\u010da.<\/p>\n<p><img decoding=\"async\" alt=\"Data Engineer i Data Scientist: \u0161to rade i koliko zara\u0111uju\" src=\"\/wp-content\/uploads\/2020\/04\/f701a922442a01286c340314861ab343.jpg\" style=\"display:block;margin: 0 auto;\" \/><br \/>\n<br \/>\nZa in\u017eenjera podataka tri su podru\u010dja posebno va\u017ena.<\/p>\n<p><b>Algoritmi i strukture podataka<\/b>. Va\u017eno je uvje\u017ebati pisanje koda i kori\u0161tenje osnovnih struktura i algoritama:<\/p>\n<ul>\n<li>analiza slo\u017eenosti algoritama,<\/li>\n<li>sposobnost pisanja razumljivog, odr\u017eivog koda,\u00a0<\/li>\n<li>paketna obrada,<\/li>\n<li>obrada u stvarnom vremenu.<\/li>\n<\/ul>\n<p>\n<b>Baze i skladi\u0161ta podataka, Business Intelligence<\/b>:<\/p>\n<ul>\n<li>pohrana i obrada podataka,<\/li>\n<li>projektiranje cjelovitih sustava,<\/li>\n<li>Data Ingestion,<\/li>\n<li>distribuirani datote\u010dni sustavi.<\/li>\n<\/ul>\n<p>\n<b>Hadoop i Big Data<\/b>. Podataka je sve vi\u0161e, a u horizontu od 3\u20125 godina ove tehnologije postat \u0107e neophodne svakom in\u017eenjeru. Osim toga:<\/p>\n<ul>\n<li>Data Lakes,<\/li>\n<li>rada s cloud pru\u017eateljima.<\/li>\n<\/ul>\n<p>\n<b>Strojno u\u010denje<\/b> \u0107e se koristiti svugdje, i va\u017eno je razumjeti koje poslovne zadatke mo\u017ee pomo\u0107i rije\u0161iti. Nije nu\u017eno znati modelirati (s tim \u0107e se baviti data scientisti), ali treba razumjeti njihovu primjenu i pripadaju\u0107e zahtjeve.<\/p>\n<h2>Koliko zara\u0111uju in\u017eenjeri i scientisti<\/h2>\n<p><\/p>\n<h3>Prihod in\u017eenjera za obradu podataka<\/h3>\n<p>\n<b>U me\u0111unarodnoj praksi<\/b> po\u010detna pla\u0107a obi\u010dno iznosi 100 000 USD godi\u0161nje i zna\u010dajno raste s iskustvom, prema podacima Glassdoora. Osim toga, tvrtke \u010desto nude opcije dionica i godi\u0161nje bonuse od 5 do 15%.<\/p>\n<p><b>U Rusiji<\/b> na po\u010detku karijere pla\u0107a obi\u010dno nije manja od 50 tisu\u0107a rubalja u regijama i 80 tisu\u0107a u Moskvi. U ovoj fazi nije potrebno iskustvo osim zavr\u0161enog obrazovanja.<\/p>\n<p>Nakon 1 do 2 godine rada \u2014 raspon je 90 do 100 tisu\u0107a rubalja.<\/p>\n<p>Raspon se pove\u0107ava na 120 do 160 tisu\u0107a nakon 2 do 5 godina. Dodaju se faktori kao \u0161to su specijalizacija prethodnih tvrtki, veli\u010dina projekata, rad s velikim podacima i drugo.<\/p>\n<p>Nakon 5 godina rada lak\u0161e je tra\u017eiti poslove u srodnim odjelima ili se prijaviti na specijalizirane pozicije kao \u0161to su:<\/p>\n<ul>\n<li>Arhitekt ili vode\u0107i programer u banci ili telekomunikacijama \u2014 oko 250 tisu\u0107a.\n<\/li>\n<\/ul>\n<p><\/p>\n<ul>\n<li>Pre-Sales u dobavlja\u010du \u010dije ste tehnologije najbolje poznavali \u2014 200 tisu\u0107a plus mogu\u0107i bonus (1 do 1,5 milijuna rubalja).\u00a0\n<\/li>\n<\/ul>\n<p><\/p>\n<ul>\n<li>Stru\u010dnjaci za implementaciju Enterprise poslovnih aplikacija, kao \u0161to su SAP \u2014 do 350 tisu\u0107a.\n<\/li>\n<\/ul>\n<p><\/p>\n<h3>Prihod data scientista<\/h3>\n<p>\n<noindex><a rel=\"nofollow\" href=\"https:\/\/vacancy.newhr.ru\/data\/data\/salary\/Normalresearch.ru_Analysts_2019.pdf\">Istra\u017eivanje<\/a><\/noindex> tr\u017ei\u0161ta analiti\u010dara tvrtke \u201eNormalne analize\u201c i agencije za zapo\u0161ljavanje New.HR pokazuje da stru\u010dnjaci za Data Science u prosjeku zara\u0111uju vi\u0161e nego analiti\u010dari drugih struka.\u00a0<\/p>\n<p>U Rusiji po\u010detna pla\u0107a data scientista s manje od godinu dana iskustva \u2014 od 113 tisu\u0107a rubalja.\u00a0<\/p>\n<blockquote><p>Kao iskustvo rada sada se tako\u0111er priznaju zavr\u0161eni programi obuke.<\/p><\/blockquote>\n<p> Nakon 1 do 2 godine takav stru\u010dnjak ve\u0107 mo\u017ee zara\u0111ivati do 160 tisu\u0107a.<\/p>\n<p>Za zaposlenika s 4 do 5 godina iskustva raspon se pove\u0107ava na 310 tisu\u0107a.<\/p>\n<h2>Kako izgledaju intervjui<\/h2>\n<p>\nNa zapadu diplomirani studenti stru\u010dnih programa prolaze prvi intervju u prosjeku 5 tjedana nakon zavr\u0161etka obuke. Oko 85% pronalazi posao unutar 3 mjeseca.<\/p>\n<p>Proces prolaska intervjua za pozicije in\u017eenjera podataka i data scientista prakti\u010dki se ne razlikuje. Obi\u010dno se sastoji od pet faza.<\/p>\n<p><b>Sa\u017eetak<\/b>Kandidatima s prethodnim neprofesionalnim iskustvom (npr. iz marketinga) potrebno je pripremiti detaljno motivacijsko pismo za svaku tvrtku ili imati preporuke od predstavnika te tvrtke.<\/p>\n<p><b>Tehni\u010dka selekcija<\/b>Obi\u010dno se provodi putem telefona. Sastoji se od jednog do dva slo\u017eena i toliko jednostavnih pitanja koja se odnose na trenutni tehnolo\u0161ki stek poslodavca.<\/p>\n<p><b>HR intervju<\/b>Mo\u017ee se provoditi putem telefona. U ovoj fazi se kandidati procjenjuju na op\u0107u prikladnost i sposobnost komunikacije.<\/p>\n<p><b>Tehni\u010dki intervju<\/b>Naj\u010de\u0161\u0107e se odvija u\u017eivo. U razli\u010ditim tvrtkama razine pozicija se razlikuju, a nazivi pozicija mogu biti razli\u010diti. Stoga se u ovoj fazi provjeravaju tehni\u010dka znanja.<\/p>\n<p><b>Intervju s tehni\u010dkim direktorom \/ glavnim arhitektom<\/b>In\u017eenjeri i znanstvenici su strate\u0161ke pozicije, a za mnoge tvrtke su to ujedno i nove pozicije. Va\u017eno je da potencijalni kolega odgovara shva\u0107anjima rukovoditelja.<\/p>\n<h2>\u0160to mo\u017ee pomo\u0107i znanstvenicima i in\u017eenjerima u karijeri<\/h2>\n<p>\nPojavilo se dovoljno novih alata za rad s podacima. Malo je onih koji jednako dobro razumiju sve.\u00a0<\/p>\n<p>Mnoge tvrtke nisu spremne zapo\u0161ljavati zaposlenike bez radnog iskustva. Me\u0111utim, kandidati s minimalnim osnovama i znanjem popularnih alata mogu ste\u0107i potrebno iskustvo ako se sami obrazuju i razvijaju.<\/p>\n<h3>Korisne osobine za data in\u017eenjera i data znanstvenika<\/h3>\n<p>\n<b>\u017delja i sposobnost u\u010denja<\/b>Nije nu\u017eno odmah \u017euriti za iskustvom ili mijenjati posao zbog novog alata, ali treba biti spreman prebaciti se na novu oblast.<\/p>\n<p><b>Te\u017enja ka automatizaciji rutinskih procesa<\/b>To je va\u017eno ne samo za produktivnost, ve\u0107 i za odr\u017eavanje visoke kvalitete podataka i brzinu njihove dostave krajnjem korisniku.<\/p>\n<p><b>Pa\u017enja i razumijevanje \"\u0161to se doga\u0111a iza kulisa\" procesa<\/b>Br\u017ee \u0107e rije\u0161iti problem onaj stru\u010dnjak koji ima pregled i detaljno poznavanje procesa.<\/p>\n<p>Osim izvrsnog poznavanja algoritama, struktura podataka i pipeline-ova, potrebno je <b>nau\u010diti razmi\u0161ljati kroz proizvode<\/b> \u2014 vidjeti arhitekturu i poslovno rje\u0161enje kao jedinstvenu sliku.\u00a0<\/p>\n<p>Na primjer, korisno je uzeti bilo koju poznatu uslugu i osmisliti bazu podataka za nju. Zatim se treba razmisliti o tome kako razviti ETL i DW koji \u0107e je napuniti podacima, tko \u0107e biti korisnici i \u0161to im je va\u017eno znati o podacima, kao i kako kupci komuniciraju s aplikacijama: za tra\u017eenje poslova i upoznavanje, iznajmljivanje automobila, aplikacija za podcaste, obrazovna platforma. <\/p>\n<blockquote><p>Pozicije analiti\u010dara, data znanstvenika i in\u017eenjera su vrlo bliske, stoga se prelazak iz jednog smjera u drugi mo\u017ee odvijati br\u017ee nego iz drugih podru\u010dja.<\/p><\/blockquote>\n<p>U svakom slu\u010daju, ljudima s IT pozadinom \u0107e biti lak\u0161e nego onima koji je nemaju. U prosjeku, odrasli motivirani ljudi se prekvalificiraju i mijenjaju posao svake 1,5\u20122 godine. To je lak\u0161e onima koji u\u010de u grupi i s mentorom, u usporedbi s onima koji se oslanjaju isklju\u010divo na otvorene izvore.<\/p>\n<h2>Od uredni\u0161tva Netologije<\/h2>\n<p>\nAko razmi\u0161ljate o zanimanju Data Engineer ili Data Scientist, pozivamo vas da istra\u017eite programe na\u0161ih te\u010dajeva:<\/p>\n<ul>\n<li>Zanimanje \u201e<noindex><a rel=\"nofollow\" href=\"https:\/\/netology.ru\/programs\/data-engineer?utm_source=habr&amp;utm_medium=externalblog&amp;utm_campaign=bds_deg_ou&amp;utm_content=14042020_dataengscientist\">Data in\u017eenjer<\/a><\/noindex>\u00bb.\u00a0\u00a0<\/li>\n<li>Zanimanje \u201e<noindex><a rel=\"nofollow\" href=\"https:\/\/netology.ru\/programs\/data-scientist\/?utm_source=habr&amp;utm_medium=externalblog&amp;utm_campaign=bds_ds_ou&amp;utm_content=14042020_dataengscientist\">Data Scientist<\/a><\/noindex>\u00bb.<\/li>\n<li>Te\u010daj \u201e<noindex><a rel=\"nofollow\" href=\"https:\/\/netology.ru\/programs\/data-analyst\/?utm_source=habr&amp;utm_medium=externalblog&amp;utm_campaign=bds_da_ou&amp;utm_content=14042020_dataengscientist\">Analiti\u010dar podataka<\/a><\/noindex>\u00bb.<\/li>\n<\/ul>\n<p>Izvor: <a content=\"nofollow\" rel=\"nofollow\" href=\"https:\/\/habr.com\/ru\/company\/netologyru\/blog\/497214\/\">habr.com<\/a> <\/p>","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"excerpt":{"rendered":"<p>\u0412\u043c\u0435\u0441\u0442\u0435 \u0441 \u0415\u043b\u0435\u043d\u043e\u0439 \u0413\u0435\u0440\u0430\u0441\u0438\u043c\u043e\u0432\u043e\u0439, \u0440\u0443\u043a\u043e\u0432\u043e\u0434\u0438\u0442\u0435\u043b\u0435\u043c \u0444\u0430\u043a\u0443\u043b\u044c\u0442\u0435\u0442\u0430 \u00abData Science \u0438 \u0430\u043d\u0430\u043b\u0438\u0442\u0438\u043a\u0430\u00bb \u0432 \u041d\u0435\u0442\u043e\u043b\u043e\u0433\u0438\u0438 \u043f\u0440\u043e\u0434\u043e\u043b\u0436\u0430\u0435\u043c \u0440\u0430\u0437\u0431\u0438\u0440\u0430\u0442\u044c\u0441\u044f, \u043a\u0430\u043a \u0432\u0437\u0430\u0438\u043c\u043e\u0434\u0435\u0439\u0441\u0442\u0432\u0443\u044e\u0442 \u043c\u0435\u0436\u0434\u0443 \u0441\u043e\u0431\u043e\u0439 \u0438 \u0447\u0435\u043c \u0440\u0430\u0437\u043b\u0438\u0447\u0430\u044e\u0442\u0441\u044f Data Scientist \u0438 Data Engineer. \u0412 \u043f\u0435\u0440\u0432\u043e\u0439 \u0447\u0430\u0441\u0442\u0438 \u0440\u0430\u0441\u0441\u043a\u0430\u0437\u0430\u043b\u0438 \u043e\u0431 \u043e\u0441\u043d\u043e\u0432\u043d\u044b\u0445 \u043e\u0442\u043b\u0438\u0447\u0438\u044f\u0445 Data Scientist \u0438 Data Engineer. \u0412 \u044d\u0442\u043e\u043c \u043c\u0430\u0442\u0435\u0440\u0438\u0430\u043b\u0435 \u043f\u043e\u0433\u043e\u0432\u043e\u0440\u0438\u043c \u043e \u0442\u043e\u043c, \u043a\u0430\u043a\u0438\u043c\u0438 \u0437\u043d\u0430\u043d\u0438\u044f\u043c\u0438 \u0438 \u043d\u0430\u0432\u044b\u043a\u0430\u043c\u0438 \u0434\u043e\u043b\u0436\u043d\u044b \u043e\u0431\u043b\u0430\u0434\u0430\u0442\u044c \u0441\u043f\u0435\u0446\u0438\u0430\u043b\u0438\u0441\u0442\u044b, \u043a\u0430\u043a\u043e\u0435 \u043e\u0431\u0440\u0430\u0437\u043e\u0432\u0430\u043d\u0438\u0435 \u0446\u0435\u043d\u0438\u0442\u0441\u044f \u0440\u0430\u0431\u043e\u0442\u043e\u0434\u0430\u0442\u0435\u043b\u044f\u043c\u0438, [&hellip;]<\/p>\n","protected":false,"gt_translate_keys":[{"key":"rendered","format":"html"}]},"author":1,"featured_media":78070,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[688],"tags":[],"class_list":["post-78069","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-administrirovanie"],"aioseo_notices":[],"aioseo_head":"\n\t\t<!-- All in One SEO 5.0.4 - aioseo.com -->\n\t<meta name=\"description\" content=\"\u0412\u043c\u0435\u0441\u0442\u0435 \u0441 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