Sr. Data Engineer

Florence software advances cures by helping the world’s most important research sites do their best work. Our solutions are now used by over 65,000 research teams in 90 countries around the world—we’re the most widely deployed site workflow tool in the industry. By the end of the decade, we’ll double the pace at which new medicines get to market by doubling the output of trial site teams.

To date, we were named a Deloitte Fast 50 business, G2 Category Leader, an Inc. & AJC best place to work, and an Inc. 5000 company five years in a row.

At Florence, we are committed to make the world a better place by accelerating research while providing an environment for our employees where they can be happy in their lives, enjoy their jobs, and grow.

Lead the design and delivery of large, cross-functional data engineering initiatives. Build scalable, reliable, secure, and well-governed data solutions that support analytics, reporting, business applications, and emerging AI/ML use cases.

• 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 Data Ops 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 Quick Sight 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, Git Hub Copilot
  , or equivalent 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.

An Ideal Candidate is/has:

Experience

• 5+ years relevant experience
• A 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.
• PostgreSQL / SQL: Advanced SQL development, query optimization, indexing, performance tuning, backup, recovery, and security.
• MongoDB: No
  
  SQL data modeling, query optimization, indexing, performance, and administration concepts.
• Snowflake: Data warehousing, performance optimization, scalable data processing, security, and cost optimization.
• dbt: ELT transformation frameworks, modular data models, testing, documentation, lineage, and CI/CD integration.
• ETL/ELT: Design, development, optimization, monitoring, and…