> Markdown version of [/jobs/ext/4707040-senior-data-engineer](https://www.wearedevelopers.com/jobs/ext/4707040-senior-data-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # SENIOR DATA ENGINEER - **Company:** H. Lee Moffitt Cancer Center - **Location:** Magnolia, TX, United States - **Experience:** Expert - **Salary:** $108,493 - $165,422 - **Contract:** Permanent contract - **Skills:** Training Data, Artificial Intelligence, Airflow, Amazon Web Services, Data Analysis, Apache HTTP Server, Automation of Tests, Microsoft Azure, Unix, Cloud Computing, Cloud Database, Code Review, Information Systems, Computer Programming, Databases, Continuous Integration, Information Engineering, Data Infrastructure, Data Integration, Data Structures, Data Systems, Data Warehousing, DevOps, Distributed Computing Environment, Integrated Development Environments, Python (Programming Language), Machine Learning, Metadata, Performance Tuning, Scrum Methodology, Cloud Services, Standard Sql, Azure Data Lake, Reverse Engineering, Software Engineering, SQL Databases, Data Streaming, Transact-SQL, Workflow Management Systems, Enterprise Data Management, Parquet, Macros, Data Ingestion, Azure Data Factory, Informatica Powercenter, Snowflake, Apache Spark, Generative AI, Git, Microsoft Fabric, Information Technology, AWS Glue, Data Analytics, Apache Kafka, Machine Learning Operations, Data Delivery, Software Version Control, Data Pipelines - **Published:** November 13, 2026 - **Apply:** https://www.careerbuilder.com/job-details/senior-data-engineer-magnolia-tx--181e2b62-0834-4236-b039-b46a6a485b0f ## About the Role Cancer, Continuous Improvement, Automation, Business Solutions, Data Analysis, Customer Experience, Artificial Intelligence (AI), Cloud Computing, Business Processes, Performance Tuning/Optimization, * Bachelor's degree in information systems, Computer Science, Business Administration, or a related field * Minimum of 8 years of experience implementing data engineering initiatives * Minimum of 3 years of cloud experience with AWS and/or Azure * Minimum of 3 years of experience working and communicating with cross-functional teams, including deriving requirements and architecting data models, shared datasets, and AI-ready data solutions Experience: * Experience with modern and traditional data integration and transformation technologies, including dbt, AWS Glue, Informatica, Azure Data Factory, and cloud-native data engineering frameworks. * Hands-on experience designing and implementing Snowflake solutions, including data ingestion, ELT, data modeling, Streams, Tasks, Dynamic Tables, Snowpipe/Snowpipe Streaming, and Snowpark, with a focus on performance, security, workload management, and cost optimization. * Strong experience with dbt, including models, incremental models, snapshots, tests, macros, packages, documentation, and reusable transformation frameworks, integrated with Git, CI/CD, and DevOps practices for automated testing, code reviews, and deployment across development, test, and production environments * Experience integrating dbt with Git, CI/CD pipelines, and DevOps practices to support automated testing, code reviews, deployment, and promotion across development, test, and production environments. * Experience with Azure data services, including Azure Data Factory, Azure Data Lake Storage (ADLS Gen2), and Microsoft Fabric, for data ingestion, orchestration, transformation, and data platform development * Experience designing and building cloud-based data lake and lakehouse architectures using AWS, Azure, Parquet, Apache Iceberg, and related cloud services * Experience designing and building AI-ready data solutions, including curated and governed data products, metadata, data quality, and data structures that support analytics, machine learning, generative AI, and AI/ML workloads * Experience working with Spark, Kafka, and Airflow for distributed processing, streaming, and workflow orchestration * Strong engineering background with expertise in data and database technologies, including advanced T-SQL and SQL * Experience working on Unix platforms and using scripting and programming languages such as Python * Experience working in Agile/SCRUM and software development environments, applying software engineering principles, code management, testing, and production support practices; ability to apply advanced data engineering principles and mentor team members * Continuously seek and apply industry best practices and emerging technologies to improve data engineering capabilities and develop new solutions ## Description The Senior Data Engineer, as a member of the Data Engineering team, will provide expertise in the design, deployment, and maintenance of data engineering frameworks that support Enterprise data needs within and external to the organization. This position advances data analytics initiatives by delivering accurate, reliable, and AI-ready data across cloud-based and on-premise environments. The Senior Data Engineer will continuously improve data engineering capabilities through innovation, modern data technologies, and self-service automation that simplifies and enhances the customer experience. This role combines technical expertise in data engineering with an understanding of business systems and processes to enable trusted data for analytics, AI, and emerging data-driven capabilities., * Perform engineering tasks including data analysis, data modeling, reverse engineering, and performance optimization * Design, develop, and deploy data pipelines and solutions supporting data warehouses, marts, and lakehouse environments * Build and maintain infrastructure for efficient extraction, transformation, and loading of data from various sources using AWS, Azure, Snowflake, SQL, and modern lakehouse architectures leveraging Parquet and Apache Iceberg * Document technical specifications, data models, process flows, standards, processes, and procedures * Define and implement data engineering strategies and process improvements to optimize performance, scalability, reliability, data quality, security, cost, data delivery, and automation * Establish and implement practices for data quality, testing, monitoring, error handling, and deployment of data solutions * Guide and mentor data engineering team members on advanced data engineering concepts, technologies, and best practices * Provide rotational on-call production support, including monitoring and facilitating resolution of issues affecting data engineering services * Ensure design, testing, documentation, and implementation practices comply with HIPAA and other patient data management policies and requirements