Максим

M ak s y m Ha leta
Ju n io r Data E n gin eer
D a ta Anal y s t with fi v e y e ar s in b anking ant i - f r a u d,
transitioning to J unior Data E ng ine e r .
D ev e l op e d hig h- p e r f or m ance S Q L q ue r ie s a n d da t a
a rchite ctur e s, op tim iz ing s e r v e r l oad and e ffic i e n c y .
D el iv e r e d p r oje cts l ike Me r chants Anal y tics b a c k e n d
a n d a g e ocod ing p ip e l ine , with e x p e r tise in E T L , da t a
[open contact info](look above in the "contact info" section) cle ans ing , and A P I inte g r ation.
P r actical AW S and A z ur e e x p e r ie nce , b u i ldi n g
[open contact info](look above in the "contact info" section) sc al ab l e d ata sol utions .
maksym-haleta Str ong p r ob l e m - s ol v ing and anal y tical s k i lls ,
Kyiv, Ukraine co m m itte d to continuous im p r ov e m e nt.

EX P ER IE NCE

E P A M L a b s (July 2024 – P resen t)

Imp l e me n t ed ETL pipelines in Azu re D ata Factory, mas terin g d a t a
in t e g ra t i o n techniques.
Wo rk e d o n A W S projects, gain in g practical experien c e with Lambda , S3,
E C2 , a n d S tep F unctions.
Fa mi l i a ri z e d with Databricks for data en g in eerin g tas ks ( th eor e t i ca l
t ra i ni n g ).

St u d e n t EP A M Uni v er s i ty (A pril 2 0 2 4 – Ju ly 2 0 2 4 )

Ga i n e d fo undational knowledg e in data en g in eerin g , in clu din g E TL / E L T
t h e o ri e s a n d data warehousing con c epts ( In mon , Kimball, an d Data Va ul t ).
Imp l e me n t ed ETL processes wi th SQL Server In teg ration Services , app l yi ng
b e s t p ra c t i ces for data extraction , tran s formation , an d loadin g .

Da t a A n a l y s t / Sy s tem s a nd Processes S upport Anal yst
J S C S e n s e B ank (May 2019 – Ma rch 2 0 2 4 )

D e v e l o p e d and optim iz ed the Merch an ts An alytic s arc h itectu re, c re a t i ng
c o mp l e x T - S QL scripts for over 5 0 ag g reg ation s , leadin g to s erver l oa d
r ed uc t i o n a nd enhanced perfor man ce.
D e s i g ne d and im plem ented a g eoc odin g pipelin e to pars e te x t ua l
ad d re s s e s , cleanse data, and efficien tly in teract with Open StreetMap API ,
im p ro v i n g a ddress data accuracy.
T ra i n e d a fr aud detection m odel for th e ban kin g app, in teg ratin g predi ct i v e
an a l y t i c s i n to banking operation s .
M a na g e d a nd analyz ed frau d preven tion ru les , improvin g res pons e
stra t e g i e s and reducing fraudu len t activity.
A p p l i e d K a n ban m ethodologies to man ag e tas ks an d optimize workflow .
 K EY P R O JE CT S & ACH IE V EMENTS

1. M e r c h a nts Ana ly ti cs D a ta b ase Back end
D e v e l o p ed high- efficiency s cripts for merc h an t-level tran s a ct i on
a g g re g a tions.
C re a t e d database objects: tables , in dexes , trig g ers , an d proc edu r e s t o
s up p o rt i nterface display an d real-time alerts .
I mp ro v e d S QL proficiency by optimizin g s erver performan ce a nd
ma na g i n g transactional cons is ten c y.

2. Ge oc odi ng Pi p eli ne f o r T ext ual Addresses
P re p a re d data for A P I reque s ts an d pars ed res u lts for databas e s tor a ge .
U t i l i z e d regular expression s to c lean s e an d s tan dardize ad d r e s s
c o mp o nents, enhancing the data in teg rity.
A d v a nc e d P ython skills throug h implemen tin g a robu s t E T L proces s .

EDU C ATI ON

T EC H N ICAL SKILLS S OF T S KI L L S

Prog ra mm i ng & D a ta Ana lysis: • Probl e m-S ol ving:
Py t h o n (A dvanced), T-SQL E fficien tly addres s in g
( Ad v a nc e d ) , S Q L (A dvanced), P a n das complex data c h allen ge s .
( Ad v a nc e d ) , N um P y (A dvanced) , • De cision- Mak ing:
Reg ul a r E xp ressions (Interm edi ate) In termediate ability to
Da t a En g i n e er i ng & E T L: make data-driven deci s i ons .
M i c ro s o ft S Q L S erver Integra tion • Communicat ion:
S e rv i c e s ( I nterm ediate), A z ure D ata Novice s kills in bu s in es s
Fa c t o ry ( N ovice), A W S (N ov ice) , corres pon den ce,
Data S t o r age & Transaction s pres en tin g , an d dialogue
( In t e rme d i a te) bu ildin g .
C l ou d P l a t f o r m s :
M i c ro s o ft A z ure (N ovice), A W S IAM, L A NG UAG ES
La mb d a , S 3 (N ovice)
F r a m e w or k s & Li b r a r i es : Flas k
• Ukrainian (native)
( In t e rme d i a te), S QLA lchemy
( In t e rme d i a te) • English (Upper- Intermediate)