Lead Data Engineering SME

Key Responsibilities:
Commercial Domain Expertise:

• Act as the primary SME for commercial operations, providing insights and leadership in Sales, Marketing, Incentive Compensation (IC), Patient Data Management, Longitudinal Access and Adjudication Data (LAAD), and Claims.
• Guide the design, implementation, and optimization of business processes in the commercial functions, ensuring alignment with company objectives and industry standards.
• Translate complex commercial business needs into actionable data solutions, ensuring that technology strategies align with business priorities.

Data Engineering & Technical Leadership:

• Lead the development and deployment of data engineering solutions within the Azure ecosystem, utilizing tools such as Azure Datalake, Azure Databricks, to manage large datasets effectively.
• Design and implement scalable, secure, and efficient data pipelines that integrate diverse commercial datasets from Sales, Marketing, Claims, Patient Data, and LAAD.
• Collaborate with cross-functional teams (including IT, Data Science, and Business Operations) to ensure seamless integration of data engineering solutions across various commercial and business functions.
• Apply expertise in SQL, Python, and other data engineering languages to build and manage data pipelines and models.

Life Sciences & Industry Knowledge:

• Deeply understand the Life Sciences sector, particularly commercial functions, including sales performance analytics, marketing effectiveness, incentive compensation, patient-centric data, and claims management.
• Work closely with business stakeholders to define KPIs, performance metrics, and reporting strategies for Sales, Marketing, and Claims processes, driving actionable insights for business decisions.
• Ensure compliance with industry regulations (e.g., HIPAA, GDPR) in all data management and analytics activities related to commercial operations.
• Provide functional leadership on the management and utilization of Longitudinal Access and Adjudication Data (LAAD), ensuring the successful integration of longitudinal data to support patient-centric commercial activities.

Collaboration & Cross-Functional Support:

• Serve as the key liaison between business and technical teams, ensuring that data solutions meet business requirements and deliver on key commercial goals.
• Collaborate with IT teams to ensure data infrastructure supports business intelligence, reporting, and analytics needs, optimizing performance, availability, and security.
• Lead the design of business intelligence solutions, including dashboards and reports, for senior commercial leadership, helping them drive data-informed decisions.

Data Governance and Strategy:

• Contribute to the development of data governance frameworks for commercial data, ensuring high data quality, security, and compliance standards are met.
• Help drive strategic initiatives related to data management, standardization, and integration across commercial functions.
• Provide guidance on best practices for data-driven decision-making and ensure alignment with both business and regulatory requirements.

Required Skills & Qualifications:
Experience:

• 8+ years of experience in commercial operations within the Life Sciences industry, with expertise in areas like Sales, Marketing, Incentive Compensation (IC), Patient Data, LAAD, and Claims.
• 5+ years of experience in Data Engineering, including hands-on experience with cloud platforms (preferably Azure), and a strong background in managing and analysing large datasets.
• Proven track record of developing and deploying data solutions that support commercial business functions in Life Sciences.

Technical Expertise:

• Proficiency in Azure Data Services (e.g., Azure Data Lake, Azure SQL, Azure Databricks, Azure Synapse).
• Expertise in SQL, Python, or other relevant data engineering languages for building data models, pipelines, and reports.
• Strong understanding of data warehousing, ETL processes, and data lake architectures.
• Familiarity with business intelligence tools such as Power BI, Tableau, or similar platforms for visualization and reporting.

Domain Knowledge:

• Extensive understanding of Life Sciences commercial functions, including Sales, Marketing, Incentive Compensation (IC), Claims, Patient
• Data Management, and Longitudinal Access and Adjudication Data (LAAD).
• Ability to translate complex Life Sciences business processes into technical requirements and solutions.
• Knowledge of industry regulations (e.g., HIPAA, GDPR, 21 CFR Part 11) and compliance considerations in managing commercial data.