We anticipate the application window for this opening will close on - 7 Apr 2025

 

At Medtronic you can begin a life-long career of exploration and innovation, while helping champion healthcare access and equity for all. You’ll lead with purpose, breaking down barriers to innovation in a more connected, compassionate world.

A Day in the Life

Do you have passion to transform organizations from data driven to value driven? Would you like to work in an environment where your technical abilities will be challenged on every project? If so, you will be a great addition to Medtronic Diabetes Data and Analytics team .We are seeking a Senior Data Engineer – data engineering to build high quality, scalable and resilient distributed systems that power Medtronic Diabetes analytics platform and data pipelines.

As a Senior Data Engineer – data engineering, you will be a hands-on data engineer, you will design and deliver scalable and high-performance data warehouse and lakehouse solutions using Databricks on AWS, leveraging best practices in data engineering, cloud-native technologies, and modern data architectures to drive impactful business outcomes.

Our mission drives the need to be a highly skilled, flexible, extremely efficient, versatile, and agile in software product development for the benefit of our patients with Type 1 and Type 2 diabetes in need of intensive insulin therapy.

Diabetes impacts over 500 million people around the world — that’s roughly one in 10 adults. That means you probably know someone affected by this disease. At Medtronic, our Diabetes Operating Unit focuses on improving the lives of those within the global diabetes community. As a business, we strive to empower people with diabetes to live life on their terms by delivering innovation that truly matters and providing support in the ways they need it. We’re committed to meeting people with diabetes where they are in their journey, always with an aim to make their lives easier. Our portfolio of innovative solutions is designed to provide customers greater freedom and better health, helping them achieve better glucose control, while spending less time managing their disease.

*This position is onsite in Duluth, GA (Hybrid/3 days per week)

Responsibilities may include the following and other duties may be assigned.

• Build, schedule, and manage data movement from application origin through batch and streaming systems to make it available for key business decisions. 
• Ensure data products are aligned with the rapidly evolving needs of business. 
• Design, build and implement efficient, analytics ready data models by transforming raw data from transactional systems (like SAP, workday, Salesforce) into performant, scalable schemas that meet analytic needs. 
• Utilize programming languages like Scala, Python and Open-Source RDBMS and NoSQL and Graph databases and Cloud based lake house services such as Databricks. 
• Design and build highly scalable data pipelines using Spark, Kafka to ingest data from various systems. 
• Graph algorithms: implement and tune graph algorithms like shortest path, centrality and community detection to extract meaningful insight from data. 
• Perform data analysis to identify trends, patterns, and insights that support business objectives, leveraging graph data queries. 
• Collaborate with and across Agile teams to design, develop, test, implement, and support technical solutions in full-stack development tools and technologies. 

Must Have Requirements:

• Minimum of 4 years of IT experience with bachelor’s in science, engineering or technical field
• OR, 2 years of data engineering experience with advanced/master’s degree  

Nice to Have:

• 5 years of hands-on experience in designing Spark pipeline with Scala or Python : this is a hands-on role  

•  3+ years in designing Airflow pipelines and deployment in Prod  

•  1+ year in managing SAAS based Privacy tools 

•  3+ years of experience in designing CI/CD pipelines either GitLab CI or GitHub actions and Databricks Asset Bundles.  

•  Proven expertise (Minimum 3 years) in designing and implementing analytics-ready data models, including dimensional and normalized schemas, with a focus on transforming raw data from complex systems (e.g.,