{"id":149583,"url":"https://alion.io/job/ipotekabank-otp-group-middlesenior-data-analyst","title":"Middle/Senior Data Analyst","company":{"id":45416,"name":"Ipotekabank OTP Group","domain":"ipotekabank.uz","url":"https://alion.io/company/ipotekabank-otp-group","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"HeadHunter","truth_index":{"grade":"B","score":80,"open_postings":30,"ghost_share":0.1,"stale_share":0,"repost_share":0.933,"time_to_fill_p50_days":29,"computed_at":"2026-10-19T05:45:15Z"}},"role":"Analytics","role_family":"Analytics","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Tashkent, Uzbekistan"],"countries":["UZ"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":13500,"max_usd":31000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":2897},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"ClickHouse","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Git","optional":false},{"name":"Greenplum","optional":false},{"name":"Pandas","optional":false},{"name":"Python","optional":false},{"name":"SQL","optional":false},{"name":"Vertica","optional":false}],"status":"live","first_seen_at":"2026-09-19T11:04:37Z","employer_posted_date":"2026-09-19","last_verified_at":"2026-10-18T20:14:46Z","board_verified":true,"closed_at":null,"days_open":30,"trust":{"level":"ok","repost_count":2,"flags":["repost"],"days_open":29},"description":"Ищем Data Analyst уровня Middle / Senior, который умеет самостоятельно работать со сложными данными DWH, глубоко понимает логику их формирования и переводит бизнес-задачи в понятные требования к данным и аналитическим витринам.\n Чем предстоит заниматься\n Проводить глубокий анализ данных, находить закономерности, аномалии и проблемы качества.\n Писать сложные SQL-запросы и строить аналитические выборки и витрины.\n Работать с DWH и Data Lake как потребитель данных.\n Разбираться в ETL/DAG и lineage, понимать логику трансформаций и взаимодействовать с Data Engineer.\n Формулировать требования к витринам и данным, участвовать в приёмке результатов.\n Контролировать качество данных и участвовать в развитии Data Governance.\n Для Senior - 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