Data Platform Engineer

Eurofins Scientific  is an international life sciences company, providing a unique range of analytical testing services to clients across multiple industries, to make life and our environment safer, healthier and more sustainable. From the food you eat, to the water you drink, to the medicines you rely on, Eurofins laboratories work with the biggest companies in the world to ensure the products they supply are safe, their ingredients are authentic and labelling is accurate. The Eurofins network of companies believes that it is a global leader in food, environment, pharmaceutical and cosmetic product testing and in discovery pharmacology, forensics, advanced material sciences and AgroScience contract research services. It is also one of the market leaders in certain testing and laboratory services for genomics, and in the support of clinical studies, as well as in biopharma contract development and manufacturing. It also has a rapidly developing presence in highly specialised and molecular clinical diagnostic testing and in-vitro diagnostic products. In over 37 years, Eurofins has grown from one laboratory in Nantes, France to over 65,000 staff across a decentralised and entrepreneurial network of more than 950 laboratories in over 1,000 companies across 59 countries. Eurofins companies offer a portfolio of over 200,000 analytical methods to evaluate the safety, identity, composition, authenticity, origin, traceability and purity of biological substances and products. In 2024, Eurofins generated total revenues of EUR 6.95 billion; and has been among the best performing stocks in Europe over the past 20 years. Company URL: Eurofins IT Delivery Center India - Eurofins Scientific Eurofins - We are Testing for Life Eurofins - IT division in enabling the business Job Title: Data & AI Platform Engineer Work Location: Bangalore Work mode: Hybrid Shift timings: 12:00 PM to 9:00 PM IST   About the role We are transforming laboratory operations toward a fully digital, data-driven and increasingly intelligent laboratory environment. The Data & AI Engineer – Digital Laboratory will build and operate the data and automation foundations that enable this transformation. The primary focus of the role is data engineering: designing reliable data pipelines, integrating laboratory and enterprise systems, building data-lake/lakehouse capabilities, developing analytics-ready data models, and automating data flows into platforms such as Power BI. At the same time, the role will have exposure to AI, machine learning, intelligent automation and emerging technologies, helping turn laboratory data into actionable insights and new digital capabilities. This is therefore not a traditional data-engineering role focused only on back-end data fixes. You will work directly on projects aimed at eliminating manual paperwork, reducing unnecessary clicks and repetitive workflows, improving laboratory efficiency, and creating the foundation for AI-enabled laboratory operations. What you will do 1. Build the Digital Laboratory Data Foundation Design, develop and maintain data pipelines connecting laboratory, IT and enterprise systems. Build and evolve data lake/lakehouse architectures for laboratory and operational data. Develop robust ETL/ELT processes using APIs, databases and other supported integration mechanisms. Transform raw laboratory data into structured, reusable and analytics-ready datasets. Develop scalable data models supporting reporting, analytics, automation and future AI/ML applications. Establish data-quality checks, monitoring, logging and error-handling mechanisms. Improve data availability, reliability, performance and traceability. 2. Laboratory Data Work with laboratory systems to enable structured extraction and analysis of relevant laboratory data. Develop solutions for extracting, transforming and analyzing various laboratory data. Partner with lab application specialists to understand data structures, workflows and business requirements. Identify opportunities to convert laboratory data into reusable enterprise data products. Support investigations of data issues and integration failures in collaboration with app and lab teams. 3. Power BI & Data Automation Automate data preparation and refresh processes supporting Power BI and management reporting. Build and maintain reliable datasets, semantic models and data pipelines. Improve the quality, consistency and usability of laboratory KPIs and management information. Develop monitoring mechanisms to proactively manage data pipelines and reporting solutions. 4. AI / ML & Intelligent Laboratory You will not be expected to be an AI researcher. Instead, you will help bring AI and ML into real lab processes. You will: Support development of ML use cases such as anomaly detection, prediction and lab performance analysis. Prepare and engineer datasets required for ML and AI applications. Work with LLMs, AI agents and RAG-based solutions where they can create practical business value. Explore opportunities to use AI to reduce manual investigation, reporting and administrative activities. Help develop intelligent solutions that can identify patterns, exceptions and opportunities for improvement. Work with the team to move successful AI/ML concepts from experimentation into production.   5. Automation & Digital Workflow Transformation Work closely with IT, laboratory and business teams to identify: Paper-based processes that can be digitized. Repetitive manual data-entry activities. Unnecessary clicks and system interactions. Manual reporting and reconciliation activities. Processes where data must be copied between systems. Opportunities for API-based integration, workflow automation, RPA or AI-agent-based execution. The objective is simple: Make the laboratory work simpler, more digital and more intelligent. 6. Production Support & Continuous Improvement This is an important part of the role. Once solutions are in production, you will: Monitor data pipelines, integrations and automated processes. Troubleshoot data-quality, pipeline and integration failures. Investigate root causes and implement permanent fixes. Manage incidents and production issues in collaboration with application owners and vendors. Maintain technical documentation and operational procedures. Imp