Job Title: Data Intelligence Analyst
Location: 100% Remote
Data Intelligence Analyst
As a(n) Data intelligence Analyst you will have the opportunity to tap into your curiosity and collaborate with some of the most innovative and diverse people around the world. Here, you will make an impact by
- Design, develop, and deploy scalable dashboards and semantic models in Power BI (Fabric-enabled), incorporating advanced features such as Dataflows Gen2, Direct Lake, and AI visuals; provide actionable insights to leadership.
- Develop and optimize data pipelines using SQL, Python (Pandas/PySpark), and modern scripting frameworks, enabling ingestion, transformation, and analysis of structured and unstructured data.
- Architect and manage cloud data solutions leveraging Snowflake, Azure Synapse, Microsoft Fabric, or Databricks, ensuring high-performance data modeling and efficient ingestion for analytics and AI use cases.
- Build and integrate AI/ML-driven insights, including:
- Predictive analytics and anomaly detection
- Natural Language Query (NLQ) and Copilot-enabled analytics
- Generative AI use cases using Azure OpenAI, AWS Bedrock, or similar platforms
- Design and consume APIs and JSON-based data pipelines to integrate enterprise systems (e.g., Salesforce, SAP, external regulatory platforms) into analytics and AI workflows.
- Conduct deep data quality and governance assessments, collaborating with Quality, Regulatory, IT, and Data Governance teams to resolve gaps and ensure alignment with enterprise data standards.
- Partner with business, Master Data, and digital transformation teams to define data products, KPIs, and AI-driven insights that support Healthcare and Regulatory operations.
- Support development of data governance frameworks, including metadata management, lineage tracking, and adherence to enterprise standards (e.g., Snowflake vs SAP BDC decision frameworks).
- Enable self-service analytics and AI adoption by training users on Power BI, Copilot, and data tools; promote best practices in data literacy and responsible AI usage.
- Contribute to cloud and AI transformation initiatives, including migration to modern data platforms, implementation of AI agents, and integration with platforms such as Azure AI Foundry or AWS Bedrock.
Your Skills and Expertise
To set you up for success in this role from day one, Solventum requires (at a minimum) the following qualifications:
Bachelor s degree or higher (completed and verified prior to start) in Computer Science, Data Science, Engineering, Statistics, Information Systems, or a related field from an accredited institution AND
(7) seven years of experience in data analytics, business intelligence, data engineering, or data science roles with demonstrated impact delivering data-driven insights.
In addition to the above requirements, the following are also required:
- Hands-on experience with data visualization tools such as Power BI (including DAX, data modeling, and dashboard design); familiarity with Power BI Fabric, Copilot, or embedded analytics is a plus.
- Experience working with modern data platforms such as Snowflake, Databricks, Azure Synapse, or Microsoft Fabric, including data ingestion and transformation.
- Proficiency in SQL and at least one programming language (Python preferred) for data manipulation, automation, and analysis (e.g., Pandas, PySpark).
Additional qualifications that could help you succeed even further in this role include:
- Master s degree in Computer Science, Data Science, Mathematics, Statistics, Finance, or a related quantitative field.
- Exposure to API integrations and JSON-based data processing to connect enterprise systems (e.g., Salesforce, SAP).
- Familiarity with AI/ML or Generative AI concepts, including experience or interest in tools such as Azure OpenAI, AWS Bedrock, or similar platforms.
- Understanding of data governance, data quality, and metadata management principles within enterprise environments.
- Demonstrated experience conducting large-scale, complex data analysis to support strategic business decision-making and operational improvements.
- Strong experience in data mining, data transformation, and feature engineering using scripting languages such as Python (Pandas, PySpark) or R.
- Experience working with streaming or near real-time data pipelines and scalable data processing frameworks is a plus.
- Working knowledge of machine learning and AI workflows, including exposure to predictive models, anomaly detection, or NLP use cases.
- Familiarity with Generative AI and LLM-based solutions (e.g., Azure OpenAI, AWS Bedrock, LangChain, or similar frameworks) is a strong plus.
Experience with API integrations, JSON data structures, and system interoperability across enterprise platforms (e.g., Salesforce, SAP).
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