Sr. Data Engineer
Florence Healthcare - US1 month ago
Atlanta, GA, USASenior
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 data pipelines and data models across PostgreSQL, MongoDB, Snowflake, and AWS using ETL/ELT and DataOps practices.
- Develop and maintain dbt transformation workflows with reusable models, testing, documentation, and lineage.
- Design and support Kafka-based real-time and event-driven data pipelines.
- Implement Master Data Management practices and enterprise data integration to improve data consistency, quality, governance, and integrity.
- Build curated datasets and semantic layers for Amazon QuickSight, Tableau, analytics, and business intelligence.
- Develop data solutions for machine learning, AI, Generative AI, RAG, and LLM-based applications, including ingestion, transformation, enrichment, embeddings, vector search, and retrieval pipelines.
- Apply data quality, lineage, security, governance, and access-control practices across analytics and AI/ML applications.
- Use AI-assisted development tools such as Claude Code, Cursor, and GitHub Copilot to improve productivity, testing, documentation, and code quality.
- Mentor engineers and contribute to technical strategy, architecture reviews, code reviews, and engineering best practices.
Requirements
- At least 5 years of relevant experience.
- Bachelor’s or master’s degree in computer science, data science, information science, or a related field, or equivalent work experience.
- Deep hands-on expertise in data engineering, cloud data platforms, data integration, and data modeling.
- Advanced PostgreSQL and SQL development, including query optimization, indexing, performance tuning, backup, recovery, and security.
- Experience with MongoDB data modeling, query optimization, indexing, performance, and administration concepts.
- Experience with Snowflake data warehousing, scalable data processing, security, performance optimization, and cost optimization.
- Experience with dbt, ELT transformation frameworks, modular data models, testing, documentation, lineage, and CI/CD integration.
- Experience designing, developing, optimizing, monitoring, and troubleshooting enterprise ETL/ELT pipelines.
- Experience with dimensional, analytical, transactional, and normalized data models.
- Experience with MDM, data standardization, data quality, governance, lineage, metadata, and observability.
- Experience with Airflow or equivalent workflow orchestration technologies and Kafka-based event-driven processing.
- Hands-on AWS cloud data engineering experience, including relevant AWS services.
- Experience preparing and serving trusted, governed datasets for BI and analytics using Amazon QuickSight and Tableau.
- Experience preparing and managing data for machine learning and AI applications, including feature engineering and training datasets.
- Familiarity with LLM data pipelines, RAG and LLM-based applications, and AI/ML data governance.
- Experience with DataOps, CI/CD, automated data testing, Git-based development, code reviews, monitoring, observability, logging, alerting, production support, and Terraform.
- Ability to collaborate with data scientists, ML engineers, and application engineers on AI/ML data requirements.
- Preferred qualifications include AWS, Snowflake, MongoDB, PostgreSQL, or dbt certifications; Databricks, Spark, Delta Lake, lakehouse, advanced Kafka, vector databases, vector search, MLOps, Amazon Bedrock, SageMaker, data observability, enterprise data governance, MDM, and large-scale analytics or BI experience.
Benefits
- Competitive compensation package.
- Medical and dental insurance.
- Office space in the heart of the city.
- Workplace diversity and nondiscrimination commitment.
Tech Stack
Categories
Data Engineering
About Florence Healthcare - US
Florence Healthcare builds a site enablement platform for clinical trials used by research sites, sponsors, and CROs. Its SaaS products include eRegulatory/eISF, eSource, and remote monitoring tools that integrate with eTMF systems to streamline study workflows and compliance (e.g., 21 CFR Part 11). Founded in 2014 and headquartered in Atlanta, the privately held company serves cancer centers and academic medical centers alongside biopharma organizations.