Principal Engineer - Data Visualization & Analytics

3383 Shire Human Genetic Therapies•Lexington, MA

2d•Onsite

About The Position

The Principal Engineer - Data Visualization & Analytics is responsible for performing complex data research and analysis to support business operations. By creating data mining architectures, statistical reporting, and data analysis methodologies, this role helps identify trends in large data sets and informs business decisions.

Requirements

Bachelor's or Master's degree in Computer Science, Data Science, Information Systems, Engineering, Mathematics, Statistics, or a related field.
5+ years of experience in data visualization, business intelligence, data analytics, analytics engineering, data science, or related roles, including responsibility for complex projects.
Advanced hands-on experience with Power BI, including report development, semantic modeling, DAX, row-level security, performance optimization, workspaces, apps, sharing, and permissions.
Advanced proficiency in SQL, with experience optimizing complex queries and database tasks.
Experience using Python or R for complex data analysis; experience with machine learning, predictive modeling, or Apache Spark is preferred.
Experience with enterprise data platforms and sources such as Databricks, SharePoint, Excel, and relational databases.
Demonstrated experience leading cross-functional analytics initiatives, influencing stakeholders, and coaching or mentoring other professionals.
Experience working in agile delivery environments; experience in a regulated industry is preferred.
Advanced Power BI expertise, including data modeling, DAX, row-level security, dashboard optimization, and governed deployment practices.
Advanced SQL skills and practical capability in Python or R for complex analysis and analytical application development.
Strong knowledge of statistical analysis, data mining, predictive modeling, machine learning, and large-scale analytics techniques.
Ability to create complex, intuitive data visualizations and impactful presentations and to coach others in these practices.
Strong understanding of visualization standards, chart selection, dashboard usability, accessibility, and data storytelling.
Ability to translate ambiguous business needs into scalable, user-friendly, governed analytical solutions and actionable recommendations.
Project leadership skills, including independent decision-making, prioritization, risk management, delegation, and coordination of small teams.
Strong governance mindset with attention to data quality, accuracy, documentation, security, and continuous improvement.
Effective communication, influencing, stakeholder management, and collaboration across technical, business, and senior audiences.
Curiosity and innovation mindset, with the ability to evaluate emerging analytics and data science practices and apply them pragmatically.

Nice To Haves

Experience with machine learning, predictive modeling, or Apache Spark is preferred.
Experience in a regulated industry is preferred.
Microsoft Power BI Data Analyst, Microsoft Fabric Analytics Engineer, or comparable data analytics certification preferred.
Relevant certification in cloud data platforms, Databricks, data science, or advanced analytics is a plus.

Responsibilities

Design, build, and maintain interactive dashboards, reports, and semantic models in Power BI.
Perform complex data research, statistical reporting, and analysis to support business operations and decision-making.
Create intuitive visualizations and presentations that communicate trends, risks, opportunities, and recommendations to technical and non-technical audiences.
Define technical architecture and roadmap for analytics, AI, and digital solutions, balancing business value, scalability, usability, and maintainability.
Develop and apply data analysis methodologies, data mining architectures, models, and protocols to identify trends in large data sets.
Optimize dashboards and analytical products for usability, accessibility, performance, and adoption using sound visualization, DAX, and modeling practices.
Partner with stakeholders to frame business questions, gather requirements, define KPIs, and translate needs into analytical and reporting solutions.
Manage large analytics projects or processes with limited oversight, including scope, priorities, risks, dependencies, and delivery outcomes.
Present insights and recommendations in a clear, actionable, and audience-appropriate manner and influence decisions through data storytelling.
Lead small project teams, coordinate cross-functional contributors, and represent the organization in project or contractual matters when required.
Coach, review, and delegate work to less-experienced professionals and participate in training junior staff.
Work with enterprise data from SQL databases, Databricks, SharePoint, Excel, and other approved sources.
Use Power BI Dataflows, semantic models, and DirectQuery or Import approaches as appropriate to the use case.
Apply advanced SQL and working knowledge of Python or R to prepare, analyze, and interpret complex data.
Apply machine learning, predictive modeling, and Apache Spark where appropriate to complex analytics use cases.
Research existing and emerging data science and analytics techniques and recommend practical innovations that improve business outcomes.
Design, develop, and deploy AI-powered business solutions leveraging Generative AI, LLMs, copilots, agentic workflows, and advanced analytics capabilities.
Develop digital products and applications using Power Platform, Dataverse, SharePoint, APIs, cloud-native services, and approved enterprise technologies.
Lead automation initiatives utilizing Power Automate, workflow orchestration, and intelligent process automation techniques.
Evaluate emerging AI, machine learning, and automation technologies and translate them into scalable business solutions with measurable business outcomes.
Collaborate with business and technical stakeholders to identify opportunities for AI adoption, operational optimization, and digital transformation.
Ensure AI and digital solutions adhere to governance, security, privacy, data quality, GxP, and Responsible AI requirements where applicable.
Ensure data quality, documentation, consistency, lineage, access control, and adherence to analytics and reporting standards.
Apply governance practices independently, advise teams on improvements, and help establish reusable patterns and standards.
Continuously monitor solution effectiveness and incorporate user feedback to improve analytical products and adoption.