Technical Architect – Data Platform & Analytics (Life Sciences)

Job Title: Technical Architect – Data Platform & Analytics (Life Sciences)Reports To: Senior Director, Data Management & AnalyticsEmployment Type: Full-timeWorkplace Type: RemoteTiming: Upto 11 pm IST (Overlap with PST) The Celito TeamThe Celito Team architects the buildout of simplified, integrated, and compliant technology stacks. With both consulting and products, our expertise can help our customers save time and money as they move from strategic Clinical & Quality management all the way to widespread and profitable commercialisation.Job OverviewThe Senior Data & Analytics Engineer / Technical Lead will serve as a key technical contributor for designing, implementing, and supporting scalable enterprise data and analytics solutions across AWS and/or Azure ecosystems. This role is responsible for leading hands-on engineering activities across modern cloud-native data platforms, including data ingestion, transformation, orchestration, reporting, and production support functions using technologies such as Databricks, Snowflake, SQL, Python, and PySpark. The individual will collaborate closely with architects, business stakeholders, analysts, and engineering teams to deliver scalable, maintainable, and analytics-ready solutions while supporting DevOps, CI/CD, operational excellence, and AI-enabled data initiatives across enterprise environments.Responsibilities and Duties Design and implement scalable enterprise data and analytics solutions across AWS and/or Azure ecosystemsAct as a hands-on technical lead contributing directly to architecture, engineering, optimisation, troubleshooting, deployments, and production support activitiesBuild and maintain ingestion, transformation, orchestration, integration, API, and reporting pipelines using modern cloud-native technologies, including Snowflake and DatabricksDesign scalable enterprise data models, semantic/business layers, and analytics-ready data structures for reporting and analytics platformsSupport analytics and reporting platforms, including Tableau, Power BI, and SpotfireSupport DevOps, CI/CD, deployment automation, release management, operational support, and environment management processesManage production support activities, including monitoring, incident management, root cause analysis, performance optimisation, and issue resolutionSupport implementation of AI/ML and GenAI-enabled analytics solutions, reusable frameworks, and AI-ready data platformsCollaborate with architects, business stakeholders, analysts, and engineering teams to deliver scalable and maintainable solutionsContribute to technical standards, engineering best practices, documentation, governance, and operational excellence initiativesQualifications Bachelor’s degree in computer science/software engineering or equivalent combination of education and experience8+ years of total IT experience6+ years of hands-on experience designing and implementing Modern Data Platforms and Analytics solutionsExperience working in Life Sciences, Pharma or MedTech domainsStrong hands-on expertise in SQL, Python, PySpark, and cloud-native data engineering technologiesStrong experience with Databricks, Snowflake, Redshift, Synapse, or similar modern data platformsExperience with AWS and/or Azure cloud servicesStrong ETL/ELT, orchestration, pipeline development, API integrations, and performance optimisation experienceExperience with Airflow, Step Functions, ADF, or similar orchestration technologiesStrong understanding of enterprise data modelling, dimensional modelling, semantic/business layer modelling, and modern data warehousing conceptsExperience supporting enterprise reporting and dashboarding solutions using Tableau, Power BI, Spotfire, or similar platformsExperience with CI/CD, Git-based workflows, deployment automation, and operational support activitiesStrong troubleshooting, incident management, debugging, optimisation, and production support capabilitiesGood communication, collaboration, ownership, and technical leadership skillsPreferred QualificationsExperience with Terraform, DevOps, and automation frameworksExposure to AI/ML, GenAI, LLMs, vector databases, or AI-enabled analytics solutionsFamiliarity with AI-ready data platforms, RAG architectures, intelligent automation, or cloud AI services

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