{"id":3449707,"url":"https://alion.io/job/ipivot-databricks-architectmanager-pharmaceuticallife-sciences-domain","title":"Databricks Architect/Manager - Pharmaceutical/Life Sciences Domain","company":{"id":4052350,"name":"iPivot","domain":"ipivot.io","url":"https://alion.io/company/ipivot","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"staff","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Bengaluru, India","Hyderabad, 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Modeling","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Git","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false}],"status":"live","first_seen_at":"2026-10-29T10:10:31Z","employer_posted_date":null,"last_verified_at":"2026-10-29T10:10:31Z","board_verified":false,"closed_at":null,"days_open":4,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":4},"description":"Hyderabad & Bengaluru - (1 days at office required in a week).\n\nWe are looking for an experienced, hands-on Databricks Architect and Data Engineering Manager with 10+ years of experience in designing and delivering scalable data engineering solutions.\n\nThe ideal candidate should be a strong individual contributor and technical lead, capable of driving solution architecture, developing reusable Python-based frameworks, designing complex data models, and leading a team of Data Engineers.\n\nKey Technical Skills & Responsibilities :\n\nAzure Databricks Architecture : \n\n- Strong hands-on experience with Azure Databricks Lakehouse Architecture, Delta Lake, Unity Catalog, Databricks Workflows/Jobs, and Databricks Asset Bundles (DAB).\n\n- Experience designing scalable, secure, and production-ready Databricks solutions.\n\nAdvanced Python & PySpark : \n\n- Expert-level proficiency in Python, PySpark, and SQL, with the ability to independently develop, review, troubleshoot, and optimize complex data engineering solutions.\n\n- Must be comfortable contributing directly to code development.\n\nReusable Python Framework Development : \n\n- Proven experience architecting and developing modular, reusable, configuration-driven Python frameworks for data ingestion, transformation, validation, orchestration, and automation.\n\n- Strong understanding of metadata-driven design, configurable business rules, extensible components, and reusable execution engines.\n\nData Modeling & Architecture : \n\n- Strong experience in conceptual, logical, and physical data modeling, relational and dimensional modeling, schema design, source-to-target mappings, CDC, SCD Type 2, and historical data processing.\n\n- Ability to independently design complex data models and develop SQL DDL scripts.\n\nLakehouse Data Engineering : \n\n- Experience designing and implementing Bronze, Silver, and Gold data architectures, Delta tables, scalable ETL/ELT pipelines, incremental processing, and enterprise data transformation frameworks.\n\nReal-Time & Batch Data Processing : \n\n- Hands-on experience building batch and streaming ingestion pipelines using Spark Structured Streaming, Auto Loader, Azure Event Hubs, Delta Change Data Feed (CDF), and related technologies.\n\nPipeline Reliability & Data Quality : \n\n- Ability to architect production-grade pipelines with robust exception handling, retries, replay, reconciliation, audit logging, data quality validations, and failure recovery mechanisms.\n\nPerformance & Cost Optimization : \n\n- Strong experience optimizing Spark workloads, SQL queries, Delta tables, cluster configurations, job execution, and overall Databricks compute costs.\n\nDevOps & Engineering Best Practices : \n\n- Experience with Git, CI/CD, Databricks Asset Bundles, automated deployments, unit testing, code quality standards, and reusable engineering practices.\n\nPharmaceutical / Life Sciences Domain : \n\n- Good understanding of pharmaceutical industry data and business processes, preferably across Commercial, Sales, Marketing, Patient Services, Clinical, or R&D domains.\n\n- Exposure to pharma datasets, data integration challenges, industry-specific business rules, and data governance requirements.\n\nTechnical Leadership & Team Management : \n\n- Ability to lead and mentor Data Engineers, provide technical direction, conduct code reviews, define engineering standards, resolve complex technical challenges, and ensure high-quality project delivery.\n\nSolution Design & Stakeholder Collaboration : \n\n- Ability to translate functional and business requirements into scalable technical architectures, prepare solution design documents, define integration patterns, and collaborate with business stakeholders, architects, DevOps, QA, and engineering teams.\n\nMandatory Qualifications :\n\n- 10+ years of overall IT experience, with significant experience in Data Engineering and Data Architecture.\n\n- Strong hands-on experience in Azure Databricks, Python, PySpark, and SQL.\n\n- Proven experience architecting and developing reusable, metadata-driven Python frameworks.\n\n- Strong expertise in enterprise data modeling and complex data pipeline design.\n\n- Experience leading Data Engineering teams while remaining actively involved in hands-on development.\n\n- Working knowledge of Pharmaceutical / Life Sciences data and business processes.\nSkills\nAzure Databricks, Data Engineering, Engineering Management, Python, 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