Sr. Data Engineer

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 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, GitHub 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:

PostgreSQL / SQL: Advanced SQL development, query optimization, indexing, performance tuning, backup, recovery, and security.

MongoDB: NoSQL 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 troubleshooting of enterprise data pipelines.

Data Modeling: Dimensional, analytical, transactional, and normalized data models.

Master Data Management (MDM): Data standardization, master/reference data, data quality, governance, and enterprise data integration.

Data Quality & Governance: Data validation, integrity, lineage, metadata, observability, and governance.

Workflow & Orchestration: Airflow or equivalent workflow orchestration technologies.

Streaming: Kafka and event-driven data processing.

Hands-on experience with the AWS ecosystem and cloud-native data engineering, including relevant services such as:

Amazon S3

Amazon MSK / Kafka

AWS Lambda

Amazon RDS

Amazon Aurora

AWS IAM

Amazon QuickSight

Ability to design secure, scalable, highly available, and cost-effective cloud data solutions using AWS services.

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, transformation, feature engineering, and training datasets.

Familiarity with LLM data pipelines and the role of data engineering in supporting LLM-based applications.

Understanding of AI/ML data governance concepts, including data quality, lineage, privacy, security, and access controls.

Experience using AI-assisted development tools such as Claude Code, Cursor, GitHub Copilot, or equivalent.

Ability to collaborate effectively with data scientists, ML engineers, and application engineers on AI/ML data requirements.

Exposure to RAG and LLM-based applications

DataOps and CI/CD practices for data pipelines and transformations.

Automated data testing and quality validation.

Git-based development and code review practices.

Infrastructure as Code using Terraform.

Monitoring, observability, logging, alerting, and production support.

Strong documentation and engineering compliance practices.

Communication — Advanced

Ownership — Advanced

Accountability — Advanced

Technical Leadership

Problem Solving

Cross-functional Collaboration

Mentoring & Knowledge Sharing

Continuous Improvement

Bonus Points if you have:

AWS, Snowflake, MongoDB, PostgreSQL, or dbt certifications

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

What’s in it for you?

Do well. We offer a competitive compensation package, medical and dental insurance, and office space in the heart of the city.

Do good. We insist that health technology is the highest calling for software development. We pride ourselves on working on something bigger than ourselves; helping advance cures and therapies.

Make the leap. Join our high-output culture to create innovative, modern, and purposeful software solutions.

Florence supports workplace diversity and does not discriminate on the basis of race, color, religion, gender identity or expression, national origin, age, military service eligibility, veteran status, sexual orientation, marital status, physical disability, or any other protected class.

Please be cautious of potential recruitment fraud. If you are interested in exploring opportunities at Florence Healthcare, please go directly to our Careers Page. Florence Healthcare will never ask you to pay a fee or download software as part of the interview process with our company. In addition, Florence Healthcare will not ask for your personal banking information until you have signed an offer of employment and completed onboarding paperwork that is provided by our People Operations team. All communications with Florence Healthcare employees will only be sent from @florencehc.com email addresses.