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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Sr. Data Engineer - **Company:** FLORENCE AND FLORENCE, LLC - **Location:** Atlanta, GA, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Airflow, Amazon Web Services, Amazon S3, Data Analysis, Backup Devices, Cloud Database, Software Quality, Code Review, Databases, Continuous Integration, Data Validation, Data Cleansing, Information Engineering, Data Governance, Data Integration, Extract Transform Load (ETL), Data Systems, Data Warehousing, Cursor, Database Testing, DevOps, Identity and Access Management, Information Sciences, PostgreSQL, Machine Learning, Metadata, MongoDB, NoSQL, Performance Tuning, Query Optimization, Reference Data, Cloud Services, DataOps, Azure Machine Learning, Search Technologies, SQL Databases, Data Streaming, Tableau (Software), Unstructured Data, Enterprise Data Management, Data Logging, Data Processing, Feature Engineering, Data Ingestion, GitHub Copilot, Retrieval-Augmented Generation, Large Language Models, Claude Code, Snowflake, Apache Spark, Generative AI, AWS Lambda, Indexer, Git, Data Layers, Event Driven Architecture, Amazon Relational Database Service, Data Lakes, Information Technology, Amazon Bedrock, Apache Kafka, Data Management, Machine Learning Operations, Video Streaming, Terraform, Data Pipelines, Databricks - **Published:** October 8, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=5ab1716679382a11 ## About the Role * 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. Data Engineering & Databases * 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. AWS & Cloud Data Engineering 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. AI/ML & Generative AI - Working Experience * 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 DevOps, DataOps & Engineering Practices * 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., * 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 ## Description * 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.