Addison, TX Job Details $140,000 - $155,000 a year 3 days ago Qualifications Performance monitoring Data model design Version control Commercial use (data warehousing systems) Data Integration (Data management) Authorization controls implementation Cloud data warehouses Technical documentation Improving system uptime or availability System performance optimization Integration Architecture Design (Architecture design skills) Application deployment Scalable systems Schema design Technology security practices Enterprise software systems development Snowflake Reducing cloud infrastructure costs Azure Data Lake Storage Cloud security best practices implementation ETL process automation Azure Data Factory Enterprise Integration Data access controls implementation Developing automated testing protocols Scalability Production monitoring Monitoring system implementation Cloud Architecture Design (Architecture design skills) Systems & applications support Full Job Description Lead Data Platform Engineer Dallas, TX | On-Site | 5 Days/Week About the Role We are seeking a Lead Data Platform Engineer to serve as the technical owner of our enterprise data platform. This is a hands-on engineering role responsible for designing, building, operating, and continuously improving the platform that delivers trusted data for business operations, reporting, and analytics. You will establish engineering standards, guide technical decisions, and ensure the platform is reliable, scalable, secure, and operationally resilient. Success in this role requires technical depth, ownership, curiosity, and a continuous improvement mindset. If you enjoy solving complex engineering challenges, building production-grade platforms, and raising the engineering bar for those around you, we'd like to meet you. What You'll Own Own the architecture, engineering, reliability, security, and day-to-day operation of the enterprise data platform. Design, build, and maintain scalable ELT pipelines using Azure Data Factory, Azure Data Lake Storage Gen2, and Snowflake. Design and evolve scalable logical and physical data models that support analytics, reporting, and operational decision-making. Design, implement, and maintain modern data transformation frameworks using dbt or equivalent, promoting reusable, testable, and maintainable data models. Establish engineering standards for data modeling, transformation, testing, deployment, monitoring, documentation, and operational support. Build reusable ingestion and transformation patterns that simplify onboarding new data sources and reduce long-term maintenance. Implement automated testing, data quality validation, lineage, and observability across the platform. Optimize platform performance, scalability, and cloud resource utilization while balancing operational cost and business value. Implement CI/CD practices that enable reliable, repeatable deployments and controlled platform changes. Ensure sensitive data is managed in accordance with HIPAA and organizational security standards through governance, auditing, and access controls. Partner with application, analytics, and business teams to deliver trusted, production-ready data solutions. Provide technical leadership through architecture reviews, code reviews, mentoring, and engineering best practices. Continuously improve the platform by identifying opportunities to automate, simplify, modernize, and reduce technical debt. Technical Expertise Required 6+ years of professional experience designing and supporting production data platforms. Strong experience with Azure Data Factory (ADF), Azure Data Lake Storage Gen2, and Microsoft Azure. Hands-on Snowflake experience, including: SQL development Streams & Tasks Performance tuning Incremental data processing Security and RBAC Strong SQL Server and advanced T-SQL development skills. Experience designing scalable ETL/ELT architectures and production-grade data integration solutions. Strong data modeling experience, including dimensional modeling and designing performant, maintainable analytical data structures. Hands-on experience with dbt or equivalent transformation frameworks (such as SQLMesh, Coalesce, Dataform, or similar), including model development, testing, documentation, and deployment. Experience implementing Git-based source control, CI/CD pipelines, and automated deployment practices. Experience designing reliable production platforms with monitoring, observability, automated testing, error handling, and recovery strategies. Understanding of cloud performance optimization, platform scalability, and FinOps principles. Preferred Python for automation, testing, or engineering utilities. Experience working in healthcare or other regulated industries. Experience with Azure DevOps or similar DevOps platforms. Experience leading technical initiatives or serving as the technical owner of an enterprise data platform. What We're Looking For We're looking for an engineer who thinks like a platform owner.
You are someone who:
Takes end-to-end ownership and accountability for production platforms. Is curious, continuously learns, and seeks better ways of solving problems. Builds scalable, maintainable, well-tested solutions rather than one-off implementations. Values clean architecture, reusable components, and engineering discipline. Balances engineering excellence with practical business outcomes. Understands that reliability, security, performance, observability, and cost efficiency are equally important in a production platform. Enjoys mentoring others, sharing knowledge, and establishing engineering standards. Communicates effectively with both technical and business stakeholders. Takes pride in leaving systems better than you found them. Technology Stack Microsoft Azure Azure Data Factory (ADF) Azure Data Lake Storage Gen2 (ADLS Gen2) Snowflake SQL Server / T-SQL dbt (or equivalent transformation framework) Git & CI/CD Python (Preferred) Qualifications Bachelor's degree in Computer Science, Information Systems, Engineering, or equivalent practical experience. Demonstrated experience owning or leading production data platforms. Experience working with regulated data environments such as HIPAA, PCI, or SOX is preferred.
