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Florence Healthcare is seeking a Senior Data Engineer to lead the design and delivery of large, cross-functional data engineering initiatives. The role focuses on building scalable, reliable, secure, and well-governed data solutions that support analytics, reporting, business applications, and emerging AI/ML use cases.
Key Responsibilities:
- Lead the design and implementation of scalable batch and real-time data pipelines
- Define and enforce standards for data modeling, reliability, testing, documentation, observability, security, and governance
- Drive root-cause analysis, performance optimization, and remediation of systemic data and platform issues
- Partner with product, engineering, analytics, data science, and business leaders on roadmap and prioritization
- Engineer scalable data pipelines and data models across PostgreSQL, MongoDB, Snowflake, and AWS using modern ETL/ELT and DataOps practices
- Develop and maintain transformation workflows using dbt, including reusable models, testing, documentation, and lineage
- Design and support real-time and event-driven data pipelines using Kafka and related streaming technologies
- Implement data integration and Master Data Management (MDM) practices to improve data consistency, quality, governance, and integrity across enterprise systems
- Build curated datasets and semantic layers that support analytics and business intelligence platforms such as Amazon QuickSight and Tableau
- Develop data solutions that support machine learning, AI, and Generative AI use cases, including preparation of structured and unstructured data
- Support RAG and LLM-based applications through data ingestion, transformation, enrichment, embeddings, vector search, and retrieval pipelines
- Apply data quality, lineage, security, governance, and access-control practices to data used across analytics and AI/ML applications
- Leverage AI-assisted development tools such as Claude Code, Cursor, or GitHub Copilot to improve engineering productivity, testing, documentation, and code quality
- Mentor engineers across multiple levels and contribute to technical strategy, architecture reviews, code reviews, and engineering best practices
About Florence Healthcare:
Florence software advances cures by helping the world's most important research sites do their best work. Solutions are used by over 65,000 research teams in 90 countries. The company has been named a Deloitte Fast 50 business, G2 Category Leader, and Inc. 5000 company five years in a row.
Requirements:
- 5+ years of relevant data engineering experience
- Bachelor's or master's degree in computer science, data science, information science, or related field (or equivalent work experience)
- Deep, hands-on expertise in data engineering, cloud data platforms, data integration, and data modeling with practical experience supporting analytics and AI/ML use cases
- Advanced SQL development, query optimization, indexing, performance tuning, backup, recovery, and security (PostgreSQL)
- NoSQL data modeling, query optimization, indexing, performance, and administration concepts (MongoDB)
- Data warehousing, performance optimization, scalable data processing, security, and cost optimization (Snowflake)
- ELT transformation frameworks, modular data models, testing, documentation, lineage, and CI/CD integration (dbt)
- Design, development, optimization, monitoring, and troubleshooting of enterprise data pipelines (ETL/ELT)
- Dimensional, analytical, transactional, and normalized data modeling
- Data standardization, master/reference data, data quality, governance, and enterprise data integration (MDM)
- Data validation, integrity, lineage, metadata, observability, and governance
- Workflow orchestration technologies such as Airflow
- Kafka and event-driven data processing
- Hands-on experience with AWS ecosystem and cloud-native data engineering (S3, MSK/Kafka, Lambda, RDS, Aurora, IAM, QuickSight)
- Ability to design secure, scalable, highly available, and cost-effective cloud data solutions
- Experience preparing and serving trusted, governed datasets for BI and analytics
- Amazon QuickSight dashboards, datasets, data preparation, and analytics
- Tableau dashboards, reporting, data sources, and analytical data models
- Strong understanding of the relationship between data engineering, semantic models, BI, and business reporting requirements
- Practical experience preparing and managing data for machine learning and AI applications
- Understanding of data preparation, feature engineering, and data quality for ML/AI
Preferred Qualifications:
- Experience with Databricks, Spark, Delta Lake, or other lakehouse technologies
- Experience with advanced Kafka/event-streaming architectures
- Experience with vector databases and vector search
- Exposure to MLOps and machine learning data pipelines
- Experience with AWS AI/ML services such as Amazon Bedrock or SageMaker
- Experience with data observability platforms
- Experience implementing enterprise data governance and MDM solutions
- Experience supporting large-scale enterprise analytics and BI environments
