Lead Data Engineer-SCM Integration & AWS Databricks

We provide our global teams with the resources needed to achieve individual career goals while helping to take science a step beyond by developing solutions for some of the world’s toughest challenges, like protecting the environment, making sure our food is safe or helping find cures for cancer.\n\nDESCRIPTION:\nJoin Thermo Fisher Scientific, the world leader in serving science. As a Business Analyst III, you'll contribute to bridging business needs with technology solutions to enable our mission of making the world healthier, cleaner and safer. Partner with stakeholders across our global organization to analyze processes, gather requirements, and implement solutions that drive operational excellence and business growth.\nThermo Fisher Scientific is seeking a Senior Data Engineer to join the Supply Chain Analytics team and help build Digital Customer Collaboration capabilities that enable end-to-end supply chain visibility and collaboration with customers.\nThis role is ideal for a hands-on data engineering professional who can design, build, and operate scalable data pipelines and analytics solutions across enterprise supply chain platforms.

The candidate will work closely with supply chain business teams, analytics partners, architects, and technology teams to connect customer, planning, procurement, manufacturing, logistics, and fulfillment data into reliable data products.\nThe role requires strong experience in Databricks, Apache Spark, Python, SQL, Airflow, and AWS-based data platforms , along with a strong understanding of data engineering best practices in an enterprise environment.\nKey Responsibilities Design, build, and maintain scalable data pipelines to support Digital Customer Collaboration and supply chain analytics use cases.\nDevelop production-grade ELT / ETL workflows using Python, SQL, Databricks, and Apache Spark .\nBuild data solutions that connect customer collaboration data with supply chain functions such as demand planning, supply planning, procurement, manufacturing, inventory, logistics, and fulfillment .\nSupport the development of data products that improve end-to-end supply chain visibility, customer connectivity, and collaboration .\nOrchestrate and monitor data pipelines using Apache Airflow or similar workflow orchestration tools.\nEngineer cloud-native data solutions using AWS services such as S3, Glue, Lambda, EMR, Redshift , or related technologies.\nApply lakehouse, Delta Lake, and medallion architecture patterns to create reliable and reusable data assets.\nOptimize Spark jobs and data pipelines for performance, scalability, reliability, and cost efficiency .\nEmbed data quality checks, monitoring, exception handling, and governance controls into data pipelines.\nPartner with supply chain analytics teams, business stakeholders, data architects, analysts, and data scientists to understand requirements and deliver data solutions.\nTranslate business requirements into technical designs, data models, pipelines, and reusable data products.\nSupport testing, deployment, production support, and continuous improvement of enterprise data solutions.\nMentor junior data engineers through code reviews, development standards, and engineering best practices.\nExperience with process improvement methodologies (e.g., Six Sigma, Lean)\nWillingness to travel up to 25% as needed\nRequired Qualifications\nBachelor’s or Master’s degree in Computer Science, Engineering, Information Technology, Data Engineering, Supply Chain, or a related field .\n6–10 years of IT experience , with strong focus on data engineering, data platforms, or analytics engineering .\nStrong hands-on experience with Databricks and Apache Spark .\nAdvanced proficiency in Python and SQL .\nExperience building scalable ELT / ETL data pipelines in an enterprise environment.\nStrong experience with Apache Airflow or similar workflow orchestration tools.\nExperience working with AWS-based data platforms , including services such as S3, Glue, Lambda, EMR, Redshift, or equivalent cloud services.\nExperience designing and operating scalable, fault-tolerant, and production-ready data pipelines.\nStrong understanding of data modeling, data integration, data quality, and data pipeline monitoring.\nAbility to work independently as a senior individual contributor while collaborating across business and technology teams.\nStrong communication skills with the ability to work with supply chain business partners, analytics teams, architects, and engineering teams.\nPreferred Qualifications\nExperience working with supply chain, customer collaboration, demand planning, supply planning, procurement, manufacturing, logistics, or inventory data .\nExperience supporting digital supply chain transformation or customer-facing analytics initiatives.\nHands-on experience with Delta Lake, lakehouse architecture, and medallion architecture patterns .\nFamiliarity with CI/CD, DataOps, version control, automated testing, and deployment practices .\nExperience building data products for enterprise analytics, dashboards, forecasting, planning, or operational reporting .\nExposure to regulated, life sciences, manufacturing, or global enterprise environments.\nExperience working with cross-functional global teams across business, analytics, and technology. Core Competencies\nStrong technical ownership and hands-on execution mindset.\nAbility to build reliable, scalable, and maintainable data solutions.\nStrong analytical and problem-solving skills.\nClear communication with both technical and business stakeholders.\nAbility to operate independently in a complex enterprise environment.\nStrong collaboration with supply chain, analytics, and technology teams.\nContinuous improvement mindset with focus on performance, quality, and business impact.\n\n]]>