Commercial Analytics Engineer Business Decision Intelligence

Franklin Lakes, NJ

Full-time

Onsite

$96K/yr - $153K/yr

Entry Level

BD is one of the largest global medical technology companies in the world. They are seeking a Commercial Analytics Engineer to design, build, and maintain dashboards and reports that provide commercial business users with accurate insights. The role involves translating business requirements into self-service BI solutions and ensuring data quality and usability.

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Responsibilities

Build and maintain interactive dashboards and standardized reports in the enterprise BI platform (e.g., Power BI, Tableau) that address recurring commercial reporting needs
Translate business user requirements into clear, accurate, and usable visualizations aligned to approved data models and metric definitions
Apply consistent design standards, layout conventions, and UX best practices to ensure reports are intuitive and accessible to non-technical users
Perform unit testing and validation of report outputs against source data to ensure accuracy before deployment
Build and maintain semantic layer objects (e.g., measures, dimensions, hierarchies, calculated fields) within the governed BI and analytics platform
Ensure semantic objects conform to approved business taxonomy, metric definitions, and naming conventions
Support the discoverability of analytics assets by maintaining metadata, descriptions, and documentation within the data catalog or BI platform
Identify and escalate inconsistencies between semantic layer definitions and source data models to senior team members
Gather and clarify requirements from commercial business users to ensure dashboard and reporting solutions meet their analytical needs
Provide first-line support for BI platform questions, report issues, and data interpretation queries from business users
Assist in user acceptance testing (UAT) of new dashboards and reports with business stakeholders
Create and maintain user guides, glossaries, and training materials to support self-service analytics adoption
Monitor dashboard and report data freshness, flagging anomalies or stale data to the appropriate pipeline owners
Participate in regular data quality reviews, logging issues and tracking resolution in alignment with team processes
Support regression testing and validation when upstream data models or pipeline changes impact existing reports
Follow team standards for version control, change management, and documentation of BI assets
Support the development and testing of agentic AI workflows that leverage governed semantic layer assets and BI data products for automated commercial insights delivery
Assist in annotating, labeling, and preparing training data sets drawn from commercial analytics outputs to support AI model development and fine-tuning efforts
Participate in prompt engineering and evaluation activities for AI-assisted reporting and analytics use cases, ensuring outputs align with approved metric definitions
Collaborate with senior analysts and Decision Science teams to validate agentic AI outputs against established BI benchmarks and data quality standards
Document agentic AI workflows, input/output specifications, and edge cases to support model governance and continuous improvement

Qualification

Required

Bachelor's degree in Business Analytics, Information Systems, Computer Science, Data Science, or a related field
1 year experience (or equivalent) in analytics, business intelligence, or a data-focused role
Proficiency in at least one enterprise BI platform (e.g., Power BI, Tableau, Looker, Qlik)
Solid SQL skills for data querying, validation, and analysis
Familiarity with semantic layer concepts including measures, dimensions, hierarchies, and calculated metrics
Experience translating business requirements into reporting and dashboard solutions
Understanding of commercial data domains such as sales pipeline, revenue, forecasting, or customer data
Strong communication skills with the ability to explain data concepts to non-technical stakeholders

Preferred

Experience with semantic layer or BI modeling tools (e.g., LookML, DAX, AtScale)
Exposure to modern cloud data platforms (e.g., Snowflake, BigQuery, Databricks)
Familiarity with data catalog or metadata management tools
Experience in MedTech, Life Sciences, or similarly regulated industries
Understanding of UX/UI design principles applied to data visualization
Familiarity with AI/ML concepts, prompt engineering, or agentic workflow tooling