AI/Data Science Engineer II

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 Data engineers are mainly tasked with transforming data into a format that can be easily analyzed and extract relevant and actionable (business) insights. They do this by developing, maintaining, and testing infrastructures for data generation. Data engineers work closely with data scientists/Machine Learning researchers and are largely in charge of architecting solutions for them that enable them to do their jobs.  Responsibilities may include the following and other duties may be assigned Design & Build Data Pipelines: Architect, implement, and optimize ETL/ELT processes to ingest data from various sources (databases, APIs, streaming systems). Data Modeling: Develop and maintain logical and physical data models in data warehouses/lakes like AWS S3 & Athena or Oracle. Deploy and manage data infrastructure on cloud platforms (AWS, Azure, or GCP) using IaC tools to ensure scalability, reliability and cost efficiency. Data Quality & Governance: Implement data validation, monitoring, and alerting frameworks to ensure accuracy, consistency, and security of data assets. Collaboration & Documentation: Understand requirements, translate business needs into technical solutions, and document data schemas, pipeline designs, and best practices. Explore and integrate innovative data tools, including LLMs and agentic systems, into the existing data ecosystem to enhance analytics and automation. Required Knowledge and Experience Bachelor’s degree in computer science, Engineering, Information Systems, or a related field (or equivalent work experience) 3 years of hands-on experience in data engineering or related roles Experience building and optimizing data pipelines, architectures, and data sets Strong understanding of ETL/ELT concepts and best practices Experience using LLMs and agentic tools for process automation Proficient in SQL and relational databases (e.g., PostgreSQL, MySQL, Oracle) Experience with big data processing frameworks (e.g., Apache Spark, Hadoop) Hands-on experience with cloud data services (e.g., AWS Glue, Azure Data Factory, GCP Dataflow) Familiarity with scripting languages such as Python (required), Scala, or Java Comfortable with shell scripting and command-line tools (e.g., Bash, CLI) Experience with version control systems, especially Git, including Git user interfaces (e.g., GitHub, GitLab) Experience with data transformation, metadata management, dependencies, and workload orchestration Knowledge of data warehousing solutions (e.g., Snowflake) Strong analytical and problem-solving skills Effective verbal and written communication skills Customer-focused mindset with the ability to adapt to evolving needs Collaborative, proactive, and able to build trust with both internal and external stakeholders Comfortable navigating ambiguity and solving complex, large-scale problems Curious, resilient, and motivated to continuously improve and explore new solutions. Preferred Qualifications Master’s degree in a relevant field. Experience with containerization and orchestration (Docker, Kubernetes). Familiarity with real-time data streaming. Understanding of data privacy and security standards (GDPR, HIPAA). Exposure to BI tools (Looker, Tableau, Power BI) and dashboard development. Certification in cloud platforms (AWS Certified Data Analytics, Google Professional Data Engineer, Azure Data Engineer Associate). Physical Job Requirements The above statements are intended to describe the general nature and level of work being performed by employees assigned to this position, but they are not an exhaustive list of all the required responsibilities and skills of this position. Benefits & Compensation Medtronic offers a competitive Sal ... (truncated, view full listing at source)