{"id":2196079,"url":"https://alion.io/job/florencehealthcare-com-sr-data-engineer","title":"Sr. Data Engineer","company":{"id":674396,"name":"Florence Healthcare","domain":"florencehealthcare.com","url":"https://alion.io/company/florencehealthcare","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":null},"role":"Data Science","role_family":"Data Science","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Atlanta, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":110000,"max_usd":210000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":1079},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Amazon Aurora","optional":false},{"name":"Amazon S3","optional":false},{"name":"Apache Kafka","optional":false},{"name":"AWS","optional":false},{"name":"AWS Lambda","optional":false},{"name":"CI/CD","optional":false},{"name":"Claude Code","optional":false},{"name":"Copilot","optional":false},{"name":"Cursor","optional":false},{"name":"dbt","optional":false},{"name":"Embeddings","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Git","optional":false},{"name":"IAM","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Master Data Management","optional":false},{"name":"PostgreSQL","optional":false},{"name":"RAG","optional":false},{"name":"Snowflake","optional":false},{"name":"SQL","optional":false},{"name":"Tableau","optional":false},{"name":"Terraform","optional":false},{"name":"Amazon SageMaker","optional":true},{"name":"AWS Bedrock","optional":true},{"name":"Databricks","optional":true},{"name":"Delta Lake","optional":true}],"status":"live","first_seen_at":"2026-10-09T18:26:43Z","employer_posted_date":"2026-10-09","last_verified_at":"2026-10-11T19:25:43Z","board_verified":true,"closed_at":null,"days_open":2,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":2},"description":"What We Do:\nFlorence 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. \nAt 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. \nWhat You’ll Bring to the Team:\nLead 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.\nYou Will:\nLead the design and implementation of scalable batch and real-time data pipelines.\nDefine and enforce standards for data modeling, reliability, testing, documentation, observability, security, and governance.\nDrive root-cause analysis, performance optimization, and remediation of systemic data and platform issues.\nPartner with product, engineering, analytics, data science, and business leaders on roadmap and prioritization.\nEngineer scalable data pipelines and data models across PostgreSQL, MongoDB, Snowflake, and AWS using modern ETL/ELT and DataOps practices.\nDevelop and maintain transformation workflows using dbt, including reusable models, testing, documentation, and lineage.\nDesign and support real-time and event-driven data pipelines using Kafka and related streaming technologies.\nImplement data integration and Master Data Management (MDM) practices to improve data consistency, quality, governance, and integrity across enterprise systems.\nBuild curated datasets and semantic layers that support analytics and business intelligence platforms such as Amazon QuickSight and Tableau.\nDevelop data solutions that support machine learning, AI, and Generative AI use cases, including preparation of structured and unstructured data.\nSupport RAG and LLM-based applications through data ingestion, transformation, enrichment, embeddings, vector search, and retrieval pipelines.\nApply data quality, lineage, security, governance, and access-control practices to data used across analytics and AI/ML applications.\nLeverage AI-assisted development tools such as Claude Code, Cursor, GitHub Copilot, or equivalent to improve engineering productivity, testing, documentation, and code quality.\nMentor engineers across multiple levels and contribute to technical strategy, architecture reviews, code reviews, and engineering best practices.\nAn Ideal Candidate is/has:\nExperience\n5+ years relevant experience\nA bachelor’s or master’s degree in computer science, data science, information science or related field, or equivalent work experience \nDeep, hands-on expertise in data engineering, cloud data platforms, data integration, and data modeling, with practical experience supporting analytics and AI/ML use cases.\nData Engineering & Databases\nPostgreSQL / SQL: Advanced SQL development, query optimization, indexing, performance tuning, backup, recovery, and security.\nMongoDB: NoSQL data modeling, query optimization, indexing, performance, and administration concepts.\nSnowflake: Data warehousing, performance optimization, scalable data processing, security, and cost optimization.\ndbt: ELT transformation frameworks, modular data models, testing, documentation, lineage, and CI/CD integration.\nETL/ELT: Design, development, optimization, monitoring, and troubleshooting of enterprise data pipelines.\nData Modeling: Dimensional, analytical, transactional, and normalized data models.\n Master Data Management (MDM): Data standardization, master/reference data, data quality, governance, and enterprise data integration.\nData Quality & Governance: Data validation, integrity, lineage, metadata, observability, and governance.\nWorkflow & Orchestration: Airflow or equivalent workflow orchestration technologies.\nStreaming: Kafka and event-driven data processing.\nAWS & Cloud Data Engineering\nHands-on experience with the AWS ecosystem and cloud-native data engineering, including relevant services such as:\nAmazon S3\nAmazon MSK / Kafka\nAWS Lambda\nAmazon RDS\nAmazon Aurora\nAWS IAM\nAmazon QuickSight\nAbility to design secure, scalable, highly available, and cost-effective cloud data solutions using AWS services.\nAnalytics & Business Intelligence\nExperience preparing and serving trusted, governed datasets for BI and analytics.\nAmazon QuickSight - dashboards, datasets, data preparation, and analytics.\nTableau - dashboards, reporting, data sources, and analytical data models.\nStrong understanding of the relationship between data engineering, semantic models, BI, and business reporting requirements.\nAI/ML & Generative AI - Working Experience\nPractical experience preparing and managing data for machine learning and AI applications.\nUnderstanding of data preparation, transformation, feature engineering, and training datasets.\nFamiliarity with LLM data pipelines and the role of data engineering in supporting LLM-based applications.\nUnderstanding of AI/ML data governance concepts, including data quality, lineage, privacy, security, and access controls.\nExperience using AI-assisted development tools such as Claude Code, Cursor, GitHub Copilot, or equivalent.\nAbility to collaborate effectively with data scientists, ML engineers, and application engineers on AI/ML data requirements.\nExposure to RAG and LLM-based applications\nDevOps, DataOps & Engineering Practices\nDataOps and CI/CD practices for data pipelines and transformations.\nAutomated data testing and quality validation.\nGit-based development and code review practices.\nInfrastructure as Code using Terraform.\nMonitoring, observability, logging, alerting, and production support.\nStrong documentation and engineering compliance practices.\nCore Strengths\nCommunication - Advanced\nOwnership - Advanced\nAccountability - Advanced\nTechnical Leadership\nProblem Solving\nCross-functional Collaboration\nMentoring & Knowledge Sharing\nContinuous Improvement\nBonus Points if you have:\nAWS, Snowflake, MongoDB, PostgreSQL, or dbt certifications\nExperience with Databricks, Spark, Delta Lake, or other lakehouse technologies\nExperience with advanced Kafka/event-streaming architectures\nExperience with vector databases and vector search\nExposure to MLOps and machine learning data pipelines\nExperience with AWS AI/ML services such as Amazon Bedrock or SageMaker\nExperience with data observability platforms\nExperience implementing enterprise data governance and MDM solutions\nExperience supporting large-scale enterprise analytics and BI environments\nWhat’s in it for you?\nDo well. We offer a competitive compensation package, medical and dental insurance, and office space in the heart of the city.\nDo 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.\nMake the leap. Join our high-output culture to create innovative, modern, and purposeful software solutions.\nFlorence 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.\nPlease 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. 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