{"id":201611,"url":"https://alion.io/job/astrazeneca-astrazenecacetdata-architect-engineering-ai-ready-data-warehouse-lead-data-business-intelligence","title":"【AstraZeneca】【CET】Data Architect Engineering – AI-Ready Data Warehouse Lead, Data & Business Intelligence","company":{"id":13195,"name":"AstraZeneca","domain":"astrazeneca.com","url":"https://alion.io/company/astra-zeneca","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"A","score":91,"open_postings":132,"ghost_share":0.008,"stale_share":0.47,"repost_share":0.106,"time_to_fill_p50_days":20,"computed_at":"2026-11-02T06:13:15Z"}},"role":"Data Science","role_family":"Data Science","seniority":"lead","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Osaka, Japan"],"countries":["JP"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":61000,"max_usd":141000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":771},"experience_years_min":10,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Amazon Redshift","optional":false},{"name":"AWS","optional":false},{"name":"Cortex","optional":false},{"name":"dbt","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Fivetran","optional":false},{"name":"Machine Learning","optional":false},{"name":"Oracle","optional":false},{"name":"Power BI","optional":false},{"name":"Snowflake","optional":false},{"name":"Streamlit","optional":false},{"name":"Prometheus","optional":true}],"status":"live","first_seen_at":"2026-09-04T14:58:48Z","employer_posted_date":"2026-09-04","last_verified_at":"2026-11-02T04:04:48Z","board_verified":true,"closed_at":null,"days_open":58,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":58},"description":"AstraZeneca’s 2030 strategy is driven by its bold ambition to pioneer scientific innovation, lead in key disease areas,\nand transform outcomes for patients worldwide. By 2030, the company aims to deliver new medicines, achieve\nindustry-leading growth, and set new standards for sustainability by becoming carbon negative. Positioned as a\nleader in leveraging technology, data, and artificial intelligence, AstraZeneca strives to advance healthcare and create\nexponential value for both patients and society. The Customer Experience & IT leads the way in shaping Japan’s\nbusiness and technology landscape, driving impactful contributions to our business success.\nAstraZeneca is investing significantly in data platforms (e.g., Snowflake-based AICE-DP) and AI,\nwhile transitioning from traditional reporting to AI-driven, interactive data applications.\nNew capabilities-such as Streamlit- and React-based Data analytics applications integrated with\nSnowflake and power BI -enable real-time data exploration, natural language insights, and self-\nservice analytics. Combined with AI layers (e.g., Cortex), data can be enriched, standardized, and\ntransformed into insights before visualization, improving speed, governance, and decision\naccuracy.\nTo fully enable this shift, strong metadata management and ontology (semantic layer) capabilities\nare essential. These ensure that business concepts (e.g., KPIs, customer definitions) are\nconsistently defined and understood by both users and AI, enabling reliable, scalable AI-driven\nanalytics and application development.\nHowever, a gap remains between business needs, data platforms, and AI capabilities, particularly\nin translating business logic into structured, AI-ready data assets and semantic models.\nThis role is critical to bridge that gap by designing and operating data platforms, metadata\nframeworks, and semantic data layers, translating business requirements into AI-ready data\nproducts and applications, and ensuring that AZ’s investments are converted into scalable,\ngoverned, and business-impacting AI use cases.\nResponsibilities\n 1. Business × Data Translation\nUnderstand AZ business domains (e.g., Commercial, Medical, RWD, CRM,Customer)\nTranslate business requirements into data models, pipelines, and architecture\nClearly communicate data solutions and rationale to stakeholders\nAligned with JD expectation: strong business understanding + logical communication\n2. Data Engineering Execution\nDesign, build, and maintain ETL/ELT pipelines\nDevelop data solutions using Snowflake, dbt, Fivetran, etc.\nEnsure data quality, consistency, and reliability\nCore Data Engineer responsibilities)\n3. AI-ready Data Platform Development\nDesign data infrastructure optimized for AI/ML use cases\nEnable scalable, real-time, and high-performance data processing\nProvide high-quality datasets for AI, advanced analytics, and Agentic AI\nAI-ready data foundation is a strategic focus area\n4. Innovation & New Technology Adoption\nEvaluate and implement new technologies (AI / ML / GenAI)\nConduct PoC (Proof of Concept) and scale successful use cases\nContinuously improve data platforms and capabilities\nInnovation and experimentation expected\n 5. Collaboration & Leadership\nCollaborate with Data Scientists, Advanced Analytics, Analysts, Business stakeholders\nWork with external partners/vendors (e.g., ZS, TCS, AZ Global DAPS)\nMentor junior engineers and contribute to engineering standards\nCross-functional collaboration and leadership required\nQualifications\nRequired:\n10+ years of experience in data architecture, data modeling, and database design. Experience with large-scale data warehousing and data lakes is essential.\nExperience in the pharmaceutical industry or in a related field where data sensitivity and compliance are critical.\nStrong communication and interpersonal skills to collaborate with various stakeholders and to translate business needs into architectural solutions.\nAbility to analyze complex data and system requirements and design comprehensive data architecture solutions.\nProven experience in designing and implementing enterprise-scale data warehouses.\nStrong knowledge of data modeling, ETL/ELT processes, and distributed systems. Like dbt,fivetran\nHands-on experience with cloud platforms (Snowflake, AWS RDS,redshift ,oracle) and modern data stack tools.\nFamiliarity with AI/ML workflows and how data architecture supports them.\nExcellent communication and leadership skills.\nCommunication with English for global stakeholders.\nPreferred:\nCertifications in data architecture, data management, or related cloud data services (e.g., AWS Certified Data Analytics, Snowflake Data Engineer).\nProven experience leading and mentoring data architecture teams and managing large-scale and complex projects.\nExperience with the latest technologies in big data, machine learning, business intelligence, and real-time analytics.\nProficiency in project management methodologies and tools.\nStrong understanding of business processes and strategic planning, with the ability to align data architecture with business objectives.\nLocation\nOsaka\n\nDate Posted\n04-9月-2026Closing Date\nAstraZeneca embraces diversity and equality of opportunity. We are committed to building an inclusive and diverse team representing all backgrounds, with as wide a range of perspectives as possible, and harnessing industry-leading skills. We believe that the more inclusive we are, the better our work will be. We welcome and consider applications to join our team from all qualified candidates, regardless of their characteristics. We comply with all applicable laws and regulations on non-discrimination in employment (and recruitment), as well as work authorization and employment eligibility verification requirements.","description_format":"text","description_chars":5764,"description_truncated":false,"requirements":{"experience_years_min":10,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[{"language":"English","level":"All levels","optional":false}]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Prescription Drugs","Oncology Therapeutics","Rare Disease Therapeutics","Cardiometabolic Therapeutics"],"lifecycle":[{"event":"open","at":"2026-09-04T14:58:48Z"}],"visa":[],"liveness":{"score":16,"band":"cold","label":"Long shot","p_open":1,"p_active":0.588,"p_room":0.28,"age_days":58,"expected_fill_days":20,"reasons":["conf:2","velocity","win:tail","crowd:brand"],"computed_at":"2026-11-02T06:13:15Z"},"pay":null,"html_url":"https://alion.io/job/astrazeneca-astrazenecacetdata-architect-engineering-ai-ready-data-warehouse-lead-data-business-intelligence","json_url":"https://alion.io/job/astrazeneca-astrazenecacetdata-architect-engineering-ai-ready-data-warehouse-lead-data-business-intelligence.json","meta":{"generated_at":"2026-11-02T08:27:59Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":11966,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-11-03T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":13195},"rest":"https://alion.io/mcp/rest/get_company?id=13195"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fastrazeneca-astrazenecacetdata-architect-engineering-ai-ready-data-warehouse-lead-data-business-intelligence"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fastrazeneca-astrazenecacetdata-architect-engineering-ai-ready-data-warehouse-lead-data-business-intelligence"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fastrazeneca-astrazenecacetdata-architect-engineering-ai-ready-data-warehouse-lead-data-business-intelligence"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/astrazeneca-astrazenecacetdata-architect-engineering-ai-ready-data-warehouse-lead-data-business-intelligence\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fastrazeneca-astrazenecacetdata-architect-engineering-ai-ready-data-warehouse-lead-data-business-intelligence"}],"agent":{"mcp":"https://alion.io/mcp/jobs","mcp_open":"https://alion.io/mcp/open","next":[{"tool":"get_job","arguments":{"id":201611}},{"tool":"search_jobs","arguments":{"company":"astrazeneca.com","detail":"compact"}},{"tool":"match_people","arguments":{"job_id":201611},"note":"people open to offers who fit this vacancy"}],"note":"The same data through MCP tools: https://alion.io/mcp/open needs no sign-in (compact searches, postings, people); https://alion.io/mcp with an Alion account has everything. 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