{"id":993539,"url":"https://alion.io/job/takeda-principal-data-engineer","title":"Principal Data Engineer","company":{"id":5984,"name":"Takeda Pharmaceutical","domain":"takeda.com","url":"https://alion.io/company/takeda","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":84,"open_postings":26,"ghost_share":0,"stale_share":0.654,"repost_share":0,"time_to_fill_p50_days":22,"computed_at":"2026-09-30T05:45:00Z"}},"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":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":31000,"max_usd":55000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":16},"experience_years_min":12,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon CloudWatch","optional":false},{"name":"Amazon ECS","optional":false},{"name":"Amazon EKS","optional":false},{"name":"Amazon EventBridge","optional":false},{"name":"Amazon Redshift","optional":false},{"name":"Amazon S3","optional":false},{"name":"AWS","optional":false},{"name":"AWS CDK","optional":false},{"name":"AWS Lambda","optional":false},{"name":"AWS Step Functions","optional":false},{"name":"CI/CD","optional":false},{"name":"CloudFormation","optional":false},{"name":"Databricks","optional":false},{"name":"Delta Lake","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Git","optional":false},{"name":"GitHub Actions","optional":false},{"name":"GitLab CI","optional":false},{"name":"HIPAA","optional":false},{"name":"IAM","optional":false},{"name":"Jenkins","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Terraform","optional":false},{"name":"Amazon Kinesis","optional":true},{"name":"Apache Kafka","optional":true},{"name":"Kubernetes","optional":true}],"status":"live","first_seen_at":"2026-09-07T00:00:00Z","employer_posted_date":"2026-09-07","last_verified_at":"2026-09-30T01:38:59Z","board_verified":true,"closed_at":null,"days_open":23,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":23},"description":"By clicking the “Apply” button, I understand that my employment application process with Takeda will commence and that the information I provide in my application will be processed in line with Takeda’s Privacy Notice and Terms of Use. I further attest that all information I submit in my employment application is true to the best of my knowledge.\nJob Description\nPrincipal Data Engineer\nAbout the Role\nWe are seeking a highly experienced Principal Data Engineer to lead the design, architecture, and implementation of enterprise-scale data platforms that power Takeda's R&D, Clinical, Regulatory, Commercial, and Enterprise Analytics ecosystems.\nAs a technical leader, you will drive the strategic direction of data engineering, define architecture standards, and deliver scalable, secure, and compliant data solutions using Databricks, AWS, PySpark, Delta Lake, and modern DataOps practices. You will partner with business stakeholders, solution architects, product owners, data scientists, and engineering teams to build reliable data products that accelerate innovation and data-driven decision making across Takeda.\nThis role requires deep expertise in modern cloud data platforms, distributed data processing, software engineering practices, and cross-functional leadership. You will mentor engineering teams, establish best practices, and ensure enterprise data solutions meet quality, scalability, security, and regulatory requirements.\nKey Responsibilities\nData Architecture & Platform Leadership\nLead architecture, design, and implementation of enterprise data platforms on Databricks and AWS.\nDefine and govern enterprise data engineering standards, reference architectures, and reusable frameworks.\nDesign scalable Lakehouse architectures using Delta Lake, Unity Catalog, Databricks Workflows, and AWS cloud services.\nCollaborate with enterprise architects and business stakeholders to translate business requirements into scalable technical solutions.\nDrive platform modernization initiatives, cloud migration programs, and data transformation roadmaps.\nEstablish best practices for data modeling, metadata management, data lineage, and governance.\nEvaluate emerging technologies and recommend improvements to Takeda's data ecosystem.\nData Engineering & Solution Delivery\nDesign and build high-performance batch and streaming (Optional) data pipelines using PySpark, Spark SQL, and Databricks.\nArchitect ingestion frameworks supporting structured, semi-structured, and unstructured data from internal and external systems.\nLead implementation of medallion architecture patterns (Bronze, Silver, Gold) to support trusted enterprise data products.\nOptimize large-scale data processing workloads to improve performance, reliability, and cost efficiency.\nDesign and implement reusable ETL/ELT frameworks and accelerator components.\nEstablish data contracts and engineering standards to ensure consistency and reliability across platforms.\nEnsure data solutions are scalable, maintainable, and aligned with enterprise architecture principles.\nCloud Engineering & AWS Platform Management\nArchitect and implement cloud-native data solutions using AWS services including:S3\nIAM\nGlue\nLambda\nECS/EKS\nStep Functions\nEventBridge\nCloudWatch\nSecrets Manager\nKMS\nRedshift\n\nDesign secure multi-account architectures and governance models.\nEstablish infrastructure automation practices using Terraform, CloudFormation, or AWS CDK.\nDrive optimization of cloud resources through cost management, workload tuning, and automation.\nPartner with cloud platform teams to ensure operational excellence and security compliance.\nDataOps, CI/CD & Engineering Excellence\nLead adoption of software engineering best practices across data engineering teams.\nDesign and implement CI/CD pipelines for data platforms using GitHub Actions, DevOps, GitLab CI, or Jenkins.\nEstablish automated deployment frameworks across Development, Test, Validation, and Production environments.\nImplement unit testing, integration testing, regression testing, and automated quality gates.\nDrive code quality initiatives including:Peer reviews\nStatic code analysis\nTest automation\nRelease management\nVersion control strategies\n\nStandardize engineering practices to improve delivery velocity and platform reliability.\nPromote Infrastructure as Code and automated environment provisioning.\nData Quality, Governance & Compliance\nEstablish enterprise data quality frameworks and monitoring capabilities.\nImplement end-to-end data lineage, metadata management, and observability solutions.\nCollaborate with governance, security, quality, and compliance teams to ensure adherence to corporate standards.\nSupport implementation of:Data governance policies\nRole-based access controls\nData retention policies\nAuditability requirements\n\nEnsure compliance with:GxP requirements\nHIPAA\n\nPromote secure handling of sensitive healthcare and research data.\nPerformance Optimization & Reliability Engineering\nEstablish platform observability using monitoring, logging, and alerting solutions.\nDefine service-level objectives and operational metrics for critical data platforms.\nLead root-cause analysis and resolution of complex production issues.\nImplement resiliency, disaster recovery, and business continuity strategies.\nContinuously improve platform performance, stability, and operational efficiency.\nLeadership, Mentoring & Cross-Functional Collaboration\nProvide technical leadership and guidance to data engineers across multiple programs and delivery teams.\nLead architectural reviews, design discussions, and engineering governance forums.\nMentor engineers in Databricks, Spark, AWS, DataOps, and software engineering best practices.\nPartner with:Product Owners\nBusiness Stakeholders\nData Scientists\nCloud Engineering Teams\nSecurity Teams\nQuality and Compliance Teams\n\nInfluence strategic data platform decisions and long-term technology roadmaps.\nDrive delivery excellence through collaboration, coaching, and continuous improvement.\nRequired Qualifications\nBachelor’s or master’s degree in computer science, Engineering, Information Systems, or related field.\n12+ years of experience in Data Engineering, Big Data, Data Platforms, or Cloud Engineering.\n4 to 5 years of experience architecting enterprise-scale data solutions on Databricks / AWS.\nDeep expertise in:Databricks\nDelta Lake\nUnity Catalog\nSpark\nPySpark\nSQL\n\nStrong AWS experience across compute, storage, security, and monitoring services.\nAdvanced Python development experience and software engineering practices.\nProven experience building large-scale enterprise data pipelines and Lakehouse architectures.\nExpertise in CI/CD implementation and Git-based development practices.\nExperience implementing automated unit testing and quality assurance frameworks.\nStrong understanding of distributed systems and performance optimization.\nExperience with Infrastructure as Code using Terraform or CloudFormation, or AWS CDK.\nExcellent communication, stakeholder management, and leadership skills.\nPreferred Qualifications\nLife Sciences, Pharmaceutical, Healthcare, or Clinical data domain experience.\nExperience supporting GxP-regulated environments.\nKnowledge of:Clinical Trial Data\nRegulatory Data\nPharmacovigilance\nReal World Data (RWD/RWE)\nOmics and Research Data\n\nExperience with ML/AI data platforms and MLOps foundations.\nExposure to streaming technologies such as Kafka, Kinesis, or Event Hubs.\nDatabricks Certified Data Engineer Professional.\nAWS Solutions Architect Professional.\nAWS Data Analytics Specialty or equivalent certifications.\nWhat Success Looks Like (First 12 Months)\nData delivery timelines are significantly reduced through reusable frameworks, automation, and CI/CD.\nData pipelines achieve high reliability, observability, and operational excellence.\nEngineering teams consistently follow software engineering, testing, and deployment best practices.\nCloud infrastructure and Databricks environments are optimized for performance, scalability, and cost.\nData products meet quality, governance, and compliance expectations across R&D and enterprise functions.\nMultiple teams successfully adopt reusable engineering accelerators developed under your leadership.\nStakeholders recognize the data platform as a strategic enabler for innovation and business outcomes.\nLocations\nIND - BengaluruWorker Type\nEmployeeWorker Sub-Type\nRegularTime Type\nFull time","description_format":"text","description_chars":8389,"description_truncated":false,"requirements":{"experience_years_min":12,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Prescription Drugs","Oncology Therapeutics","Neurology & CNS Therapeutics","Rare Disease 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