{"id":1579965,"url":"https://alion.io/job/thermo-fisher-scientific-sr-data-platform-engineer","title":"Sr. Data Platform Engineer","company":{"id":7922,"name":"Thermo Fisher Scientific","domain":"thermofisher.com","url":"https://alion.io/company/thermo-fisher-scientific","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":78,"open_postings":149,"ghost_share":0,"stale_share":0.893,"repost_share":0,"time_to_fill_p50_days":28,"computed_at":"2026-10-20T05:45:00Z"}},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":"full_time","work_mode":"hybrid","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":22000,"max_usd":45000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":71},"experience_years_min":10,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"Delta Lake","optional":false},{"name":"Embeddings","optional":false},{"name":"ETL/ELT","optional":false},{"name":"GCP","optional":false},{"name":"IAM","optional":false},{"name":"Looker","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"Platform Engineering","optional":false},{"name":"Power BI","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Spark","optional":false},{"name":"SQL","optional":false},{"name":"Tableau","optional":false},{"name":"Terraform","optional":false},{"name":"Airflow","optional":true},{"name":"Apache Kafka","optional":true},{"name":"AWS Bedrock","optional":true},{"name":"Azure Data Factory","optional":true},{"name":"Fine-tuning","optional":true},{"name":"Hugging Face","optional":true},{"name":"LangChain","optional":true},{"name":"LlamaIndex","optional":true},{"name":"OpenAI","optional":true}],"status":"live","first_seen_at":"2026-10-01T12:33:49Z","employer_posted_date":"2026-10-01","last_verified_at":"2026-10-21T00:54:55Z","board_verified":true,"closed_at":null,"days_open":19,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":19},"description":"Work Schedule\nStandard (Mon-Fri)Environmental Conditions\nOfficeJob Description\nJoin Thermo Fisher Scientific, the world leader in serving science, as a Staff Engineer, Software to make a meaningful impact. In this role, you will provide technical leadership and architectural guidance while developing innovative software solutions that enable our customers to make the world healthier, cleaner, and safer. Working in a supportive, multi-functional environment, you will design and implement sophisticated solutions across our product portfolio, from cloud platforms to scientific instrumentation. You will support the growth of other engineers, drive adoption of best practices, and help shape the technical direction of critical projects. This position offers the opportunity to work with advanced technologies while contributing to groundbreaking scientific discoveries.\nDescription\nExperience: 10+ years of professional experience building analytics platforms, including 5+ years of hands-on Databricks experience\nRole: Senior Data Platform Engineer\nPrimary Skills: Databricks, Apache Spark/PySpark, Python, SQL, Delta Lake, Unity Catalog, MLflow, GenAI, BI, Cloud, CI/CD, Terraform\nRole Overview\nWe are seeking an experienced Sr. Data Platform Engineer to build and operate secure, scalable data platforms and deliver reliable data, AI, generative AI, and analytics solutions using Databricks, Apache Spark, and modern cloud technologies.\nThe role combines platform engineering, data engineering, AI/ML enablement, business intelligence, and operational support, working closely with engineering, data science, analytics, security, DevOps, architecture, and business stakeholders.\nKey Responsibilities\nOutcome 1: Secure, scalable, and governed Databricks platform\nConfigure and manage Databricks workspaces, clusters, policies, runtimes, and separate Development, QA/UAT, and Production environments.\nImplement platform security and governance using Unity Catalog, IAM/RBAC, service principals, secrets, private connectivity, and enterprise access controls.\nAutomate Databricks infrastructure and deployments using Terraform, source control, and CI/CD practices.\nOutcome 2: Reliable and high-performing data products\nBuild and maintain scalable ETL/ELT pipelines and Lakehouse solutions using Apache Spark, PySpark, Databricks workflows/jobs, and Delta Lake.\nIntegrate Databricks with enterprise data sources, cloud storage, databases, APIs, and downstream applications.\nOptimize Spark workloads, queries, clusters, and resource usage for performance, reliability, scalability, and cost.\nOutcome 3: Production-ready AI, machine learning, and generative AI solutions\nPrepare data and build ML workflows covering feature engineering, model training, evaluation, deployment, and lifecycle management using Databricks and MLflow.\nDevelop generative AI solutions using LLMs, prompt engineering, embeddings, vector search, retrieval-augmented generation, and model serving.\nEvaluate and monitor AI solutions for accuracy, relevance, safety, bias, latency, cost, privacy, and governance.\nOutcome 4: Trusted analytics and decision support\nCreate analytical datasets, semantic models, KPIs, dashboards, and reporting layers using Databricks SQL and tools such as Power BI, Tableau, or Looker.\nTranslate business requirements into accurate, performant, user-friendly dashboards and self-service analytics solutions.\nEnsure analytics solutions follow applicable data governance, security, quality, and accessibility standards.\nOutcome 5: Operable and reusable platform capabilities\nEstablish monitoring, logging, alerting, troubleshooting, and operational support for data, AI, generative AI, and analytics workloads.\nCollaborate across architecture, security, infrastructure, engineering, data science, analytics, and business teams to define practical Databricks standards and best practices.\nMaintain concise technical documentation for platform configuration, deployment, data pipelines, AI workflows, dashboards, and operational procedures.\nRequired Technical Skills\nAt least 10 years of professional experience building analytics platforms, including at least 5 years of hands-on Databricks experience; a Databricks certification is required.\nStrong production experience with Databricks, Apache Spark/PySpark, Python, SQL, Delta Lake, Lakehouse architecture, workspaces, clusters, jobs/workflows, and scalable ETL/ELT pipelines.\nExperience with Azure, AWS, or Google Cloud, including cloud storage, Unity Catalog, IAM/RBAC, secrets management, networking, and secure connectivity.\nExperience with Terraform or similar Infrastructure-as-Code tools, CI/CD, and source-control practices.\nWorking experience with MLflow and AI/ML/GenAI delivery, including model lifecycle, LLMs, embeddings, vector search, RAG, prompt engineering, and model serving.\nExperience with Databricks SQL and BI tools such as Power BI, Tableau, or Looker, including semantic models and KPIs.\nStrong troubleshooting, performance optimization, monitoring, and production-support skills.\nPreferred Skills\nExperience designing enterprise-scale Databricks Lakehouse platforms and standardized multi-environment deployments.\nExperience with Databricks Asset Bundles, Apache Airflow, Azure Data Factory, or similar deployment and orchestration frameworks.\nExperience with streaming technologies such as Spark Structured Streaming, Kafka, or Event Hubs.\nExperience with advanced MLOps and GenAI tooling such as LangChain, LlamaIndex, Hugging Face, Azure OpenAI, Amazon Bedrock, fine-tuning, or model evaluation.\nKnowledge of data governance, metadata management, data-quality frameworks, BI governance, privacy controls, and responsible AI practices.\nAdditional Databricks certifications or relevant cloud certifications, and experience in regulated or large enterprise environments, are a plus.\nBehavioral Competencies\nStrong analytical, troubleshooting, and outcome-oriented problem-solving skills.\nStrong ownership of reliable, secure, scalable, and maintainable solutions.\nAbility to collaborate effectively across engineering, data science, analytics, architecture, DevOps, security, and business teams.\nAbility to translate business needs into practical data, analytics, and AI solutions.\nClear verbal and written communication, including concise technical documentation.\nStrong attention to data governance, security, privacy, responsible AI, and operational discipline.\nEducation\nBachelor’s or master’s degree in computer science, Information Technology, Engineering, or a related 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