Senior Data Engineer

• Senior Data Engineer
• 📍 Location: Tampa, Florida, United States
• 🏢 Industry: Pharmaceutical manufacturing
• 💼 Work Setting: Hybrid

• Are You seeking an experienced Senior Data Engineer to design, develop, and optimize enterprise data solutions supporting clinical, pre-clinical, and translational research environments. This role is responsible for integrating complex scientific and healthcare datasets, developing scalable cloud-based data pipelines, and enabling advanced analytics through modern data architecture and engineering practices.
• The ideal candidate will possess strong expertise in cloud technologies, data warehousing, ETL development, and life sciences data management, with the ability to collaborate closely with business, research, and scientific stakeholders to deliver high-quality data products and actionable insights.

• Key Responsibilities
• Data Engineering & Integration
• Design, develop, and maintain scalable data products that integrate information from multiple source systems.
• Build cloud-native data pipelines that support high-quality, reliable, and performant data delivery.
• Develop and implement data integration solutions across enterprise platforms and research environments.
• Ensure consistency, accessibility, and scalability of data assets throughout the organization.
• Cloud Data Platform Development
• Develop and manage data solutions utilizing cloud-based data lakes and data warehouses.
• Support migration and modernization of data platforms from on-premises environments to cloud architectures.
• Design scalable data processing frameworks that support large volumes of scientific and operational data.
• Implement best practices for cloud-based data management, governance, and performance optimization.
• ETL & Data Pipeline Development
• Design and develop extraction, transformation, and loading (ETL) processes that support enterprise data initiatives.
• Create efficient incremental loading strategies to improve data processing performance and system scalability.
• Optimize data workflows for reliability, maintainability, and operational efficiency.
• Support data ingestion, transformation, validation, and distribution processes.
• Data Modeling & Requirements Analysis
• Partner with business and scientific stakeholders to gather requirements and translate them into technical specifications and mapping documents.
• Define data models, integration requirements, and business rules for research and enterprise data solutions.
• Develop solutions that support clinical, laboratory, and translational research data needs.
• Ensure alignment between business requirements and technical implementations.
• Data Quality & Testing
• Lead unit testing, data validation, quality assurance, and troubleshooting activities.
• Identify and resolve data quality issues across multiple systems and platforms.
• Establish controls and monitoring processes that improve data accuracy and integrity.
• Support ongoing improvements to data governance and data quality frameworks.
• Research Data & Scientific Collaboration
• Collaborate with scientific and research teams to support integration and analysis of:
• Clinical datasets
• Pre-clinical research data
• Biospecimen information
• Biomarker datasets
• Translational research data
• Patient annotation information
• Support data strategies that enhance research, development, and scientific decision-making.
• Enterprise Data Architecture
• Support enterprise data warehousing and analytics initiatives.
• Contribute to modern data architecture approaches including decentralized and domain-driven data management strategies.
• Promote standardized data integration practices across multiple data platforms.
• Ensure solutions are scalable, secure, and aligned with organizational goals.
• Reporting & Visualization Support
• Enable analytics and reporting capabilities through well-structured data models and curated datasets.
• Support visualization and business intelligence initiatives.
• Develop data structures that facilitate self-service analytics and advanced reporting.

Qualifications

• Required
• Bachelor's degree in:
• Computer Science
• Information Systems
• Data Engineering
• Related technical discipline
• 5+ years of progressive experience in:
• Data Engineering
• Data Warehousing
• ETL Development
• Cloud Data Platforms
• Enterprise Data Integration
• Experience migrating enterprise data environments from on-premises systems to cloud platforms.
• Strong experience designing and implementing scalable data solutions.
• Technical Expertise
• Cloud Technologies
• Experience with cloud services and data platforms such as:
• Data Lakes
• Data Warehouses
• Serverless Data Processing
• Cloud-Based Data Integration Services
• Data Engineering & ETL
• Expertise developing:
• ETL Pipelines
• Data Transformation Frameworks
• Incremental Load Processes
• Data Integration Solutions
• Strong knowledge of large-scale data processing architectures.
• Databases & Query Optimization
• Experience working with:
• Relational Databases
• NoSQL Databases
• Data Warehouses
• Distributed Data Platforms
• Strong SQL development and query performance optimization skills.
• Big Data & Analytics
• Experience with:
• Distributed Data Processing Frameworks
• Real-Time Analytics Solutions
• Data Lake Architectures
• Enterprise Reporting Platforms
• Knowledge of data visualization and analytics tools.
• Life Sciences Domain Knowledge
• Experience supporting:
• Clinical Research
• Preclinical Research
• Translational Medicine
• Biomarker Programs
• Biologics Development
• Scientific Data Management
• Understanding of research and development data standards and best practices within regulated environments.

• Preferred Qualifications
• Experience supporting pharmaceutical, biotechnology, healthcare, or life sciences organizations.
• Knowledge of data governance and data mesh frameworks.
• Experience integrating scientific, laboratory, and clinical datasets.
• Exposure to modern analytics, machine learning, and cloud-native data architectures.
• Strong understanding of research data lifecycle management.

• Core Competencies
• Data Engineering
• Cloud Data Architecture
• ETL Development
• Data Warehousing
• Data Quality Management
• Clinical & Research Data Integration
• Big Data Technologies
• Data Modeling
• Analytics Enablement
• Stakeholder Collaboration
• Problem Solving
• Process Optimization
• Scientific Data Management
• Enterprise Data Strategy