Insulet Corporation
Analytics Engineer - Acton, Mass. (Hybrid)
Insulet Corporation, Acton, Massachusetts, us, 01720
Analytics Engineer
Insulet Corporation, maker of the OmniPod, is the leader in tubeless insulin pump technology. The Analytics Engineer role focuses on designing, building, and maintaining scalable data infrastructure and analytics solutions to support global data initiatives aligned with Insulet's overall data strategy. Collaborating with senior analytics leaders and cross-functional teams, this role involves creating advanced data pipelines, optimizing data workflows, and enabling effective reporting and visualization tools. The position combines cloud infrastructure expertise, data engineering practices, and analytics development to deliver actionable insights across core business functions such as R&D, clinical, medical affairs, quality, and global manufacturing. Responsibilities:
Design and implement robust, scalable, and efficient data pipelines for ingestion, transformation, and processing of large datasets.
Create and maintain analytics solutions and dashboards within Power BI for dynamic reporting and data-driven decision-making.
Work with IT and other teams to optimize data storage, retrieval, and integration using cloud platforms such as Azure, AWS, or GCP.
Develop automation workflows for data processes, ensuring high accuracy and performance.
Collaborate with cross-functional teams to define requirements for effective data models and architecture.
Ensure data security, quality, and governance practices are adhered to across workflows.
Conduct performance monitoring, debugging, and optimization of data infrastructure components.
Partner with stakeholders to deploy and validate analytics tools, including efforts in Installation Qualification (IQ), Operational Qualification (OQ), and Performance Qualification (PQ).
Provide documentation and training to business units on effective use of developed analytics solutions.
Technical Requirements:
Expertise in data pipeline design and development using tools like ETL frameworks, SQL, and cloud platforms such as Azure Data Factory, AWS Glue, or Google Dataflow.
Advanced proficiency in Power BI for creating interactive dashboards and reporting solutions.
Proficiency in scripting languages such as Python, R, or PySpark for data manipulation and automation tasks.
Strong experience with relational databases (e.g., SQL Server, Oracle, Teradata) and exposure to NoSQL databases (e.g., MongoDB).
Familiarity with CI/CD workflows to deploy data solutions effectively.
Knowledge of software engineering principles and data visualization best practices.
Functional knowledge of cloud infrastructure and distributed computing systems.
Soft Skills:
Strong analytical thinking and problem-solving skills.
Ability to work independently and take ownership of technical initiatives while collaborating with cross-functional teams.
Clear communication skills to articulate technical concepts to non-technical stakeholders.
Commitment to delivering high-quality solutions that align with business goals.
Effective documentation and training abilities to ensure adoption of analytics tools and processes.
Recommended Areas of Courses:
Data Engineering Fundamentals (e.g., ETL design, pipeline optimization).
Cloud Platforms for Data Processing (e.g., Azure Data Factory, AWS Glue, or Google BigQuery).
Advanced Power BI analytics techniques.
Automation and CI/CD in data workflows.
Data Security and Governance Practices.
Statistical Modeling and Predictive Analytics Fundamentals.
The focus on technical implementation and infrastructure in this revised job profile aligns with the expertise expected of an Analytics Engineer, while preserving the collaborative and cross-functional nature of the role.
Insulet Corporation, maker of the OmniPod, is the leader in tubeless insulin pump technology. The Analytics Engineer role focuses on designing, building, and maintaining scalable data infrastructure and analytics solutions to support global data initiatives aligned with Insulet's overall data strategy. Collaborating with senior analytics leaders and cross-functional teams, this role involves creating advanced data pipelines, optimizing data workflows, and enabling effective reporting and visualization tools. The position combines cloud infrastructure expertise, data engineering practices, and analytics development to deliver actionable insights across core business functions such as R&D, clinical, medical affairs, quality, and global manufacturing. Responsibilities:
Design and implement robust, scalable, and efficient data pipelines for ingestion, transformation, and processing of large datasets.
Create and maintain analytics solutions and dashboards within Power BI for dynamic reporting and data-driven decision-making.
Work with IT and other teams to optimize data storage, retrieval, and integration using cloud platforms such as Azure, AWS, or GCP.
Develop automation workflows for data processes, ensuring high accuracy and performance.
Collaborate with cross-functional teams to define requirements for effective data models and architecture.
Ensure data security, quality, and governance practices are adhered to across workflows.
Conduct performance monitoring, debugging, and optimization of data infrastructure components.
Partner with stakeholders to deploy and validate analytics tools, including efforts in Installation Qualification (IQ), Operational Qualification (OQ), and Performance Qualification (PQ).
Provide documentation and training to business units on effective use of developed analytics solutions.
Technical Requirements:
Expertise in data pipeline design and development using tools like ETL frameworks, SQL, and cloud platforms such as Azure Data Factory, AWS Glue, or Google Dataflow.
Advanced proficiency in Power BI for creating interactive dashboards and reporting solutions.
Proficiency in scripting languages such as Python, R, or PySpark for data manipulation and automation tasks.
Strong experience with relational databases (e.g., SQL Server, Oracle, Teradata) and exposure to NoSQL databases (e.g., MongoDB).
Familiarity with CI/CD workflows to deploy data solutions effectively.
Knowledge of software engineering principles and data visualization best practices.
Functional knowledge of cloud infrastructure and distributed computing systems.
Soft Skills:
Strong analytical thinking and problem-solving skills.
Ability to work independently and take ownership of technical initiatives while collaborating with cross-functional teams.
Clear communication skills to articulate technical concepts to non-technical stakeholders.
Commitment to delivering high-quality solutions that align with business goals.
Effective documentation and training abilities to ensure adoption of analytics tools and processes.
Recommended Areas of Courses:
Data Engineering Fundamentals (e.g., ETL design, pipeline optimization).
Cloud Platforms for Data Processing (e.g., Azure Data Factory, AWS Glue, or Google BigQuery).
Advanced Power BI analytics techniques.
Automation and CI/CD in data workflows.
Data Security and Governance Practices.
Statistical Modeling and Predictive Analytics Fundamentals.
The focus on technical implementation and infrastructure in this revised job profile aligns with the expertise expected of an Analytics Engineer, while preserving the collaborative and cross-functional nature of the role.