Thermo Fisher Scientific seeks a Senior Data Platform Engineer with 10+ years of analytics platform experience and 5+ years of hands-on Databricks expertise. This role involves designing, building, and operating secure, scalable data platforms that deliver reliable data, AI, and analytics solutions across the organization using modern cloud technologies.
Responsibilities
Configure and manage Databricks workspaces, clusters, policies, and environments; implement security using Unity Catalog and RBAC
Build and maintain scalable ETL/ELT pipelines and Lakehouse solutions; optimize Spark workloads for performance and cost
Develop and evaluate production-ready AI, machine learning, and generative AI solutions using MLflow and LLMs
Create analytical datasets, dashboards, and self-service analytics solutions aligned with business requirements and governance standards
Establish monitoring, logging, and operational support; collaborate across teams to define Databricks standards and best practices
Requirements
10+ years professional experience building analytics platforms; 5+ years hands-on Databricks experience; Databricks certification required
Production expertise with Databricks, Apache Spark/PySpark, Python, SQL, Delta Lake, and scalable ETL/ELT pipelines
Cloud platform experience (Azure, AWS, or Google Cloud) with IAM/RBAC, secrets management, and secure connectivity
Terraform or Infrastructure-as-Code proficiency, CI/CD practices, and source control experience
MLflow and AI/ML/GenAI delivery experience including model lifecycle, LLMs, embeddings, vector search, and RAG
Databricks SQL and BI tools experience (Power BI, Tableau, or Looker) with semantic models and KPIs
Strong troubleshooting, performance optimization, monitoring, and production-support capabilities
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