Lead Data Engineer - Supply Chain Management Integration and AWS Databricks

Lead Data Engineer-SCM Integration & AWS Databricks

Work Schedule

Standard (Mon-Fri)

Environmental Conditions

Office

Job Description

As part of the Thermo Fisher Scientific team, you’ll discover meaningful work that makes a positive impact on a global scale. Join our colleagues in bringing our Mission to life every single day to enable our customers to make the world healthier, cleaner and safer. 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.

DESCRIPTION:
Join 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.

Thermo 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.

This 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.

The 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.

Key Responsibilities

• Design, build, and maintain scalable data pipelines to support Digital Customer Collaboration and supply chain analytics use cases.
• Develop production-grade ELT / ETL workflows using Python, SQL, Databricks, and Apache Spark.
• Build data solutions that connect customer collaboration data with supply chain functions such as demand planning, supply planning, procurement, manufacturing, inventory, logistics, and fulfillment.
• Support the development of data products that improve end-to-end supply chain visibility, customer connectivity, and collaboration.
• Orchestrate and monitor data pipelines using Apache Airflow or similar workflow orchestration tools.
• Engineer cloud-native data solutions using AWS services such as S3, Glue, Lambda, EMR, Redshift, or related technologies.
• Apply lakehouse, Delta Lake, and medallion architecture patterns to create reliable and reusable data assets.
• Optimize Spark jobs and data pipelines for performance, scalability, reliability, and cost efficiency.
• Embed data quality checks, monitoring, exception handling, and governance controls into data pipelines.
• Partner with supply chain analytics teams, business stakeholders, data architects, analysts, and data scientists to understand requirements and deliver data solutions.
• Translate business requirements into technical designs, data models, pipelines, and reusable data products.
• Support testing, deployment, production support, and continuous improvement of enterprise data solutions.
• Mentor junior data engineers through code reviews, development standards, and engineering best practices.
• Experience with process improvement methodologies (e.g., Six Sigma, Lean)
• Willingness to travel up to 25% as needed

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, Information Technology, Data Engineering, Supply Chain, or a related field.
• 6–10 years of IT experience, with strong focus on data engineering, data platforms, or analytics engineering.
• Strong hands-on experience with Databricks and Apache Spark.
• Advanced proficiency in Python and SQL.
• Experience building scalable ELT / ETL data pipelines in an enterprise environment.
• Strong experience with Apache Airflow or similar workflow orchestration tools.
• Experience working with AWS-based data platforms, including services such as S3, Glue, Lambda, EMR, Redshift, or equivalent cloud services.
• Experience designing and operating scalable, fault-tolerant, and production-ready data pipelines.
• Strong understanding of data modeling, data integration, data quality, and data pipeline monitoring.
• Ability to work independently as a senior individual contributor while collaborating across business and technology teams.
• Strong communication skills with the ability to work with supply chain business partners, analytics teams, architects, and engineering teams.

Preferred Qualifications

• Experience working with supply chain, customer collaboration, demand planning, supply planning, procurement, manufacturing, logistics, or inventory data.
• Experience supporting digital supply chain transformation or customer-facing analytics initiatives.
• Hands-on experience with Delta Lake, lakehouse architecture, and medallion architecture patterns.
• Familiarity with CI/CD, DataOps, version control, automated testing, and deployment practices.
• Experience building data products for enterprise analytics, dashboards, forecasting, planning, or operational reporting.
• Exposure to regulated, life sciences, manufacturing, or global enterprise environments.
• Experience working with cross-functional global teams across business, analytics, and technology.

  Core Competencies

• Strong technical ownership and hands-on execution mindset.
• Ability to build reliable, scalable, and maintainable data solutions.
• Strong analytical and problem-solving skills.
• Clear communication with both technical and business stakeholders.
• Ability to operate independently in a complex enterprise environment.
• Strong collaboration with supply chain, analytics, and technology teams.
• Continuous improvement mindset with focus on performance, quality, and business impact.

Experience Level

Senior Level