Senior Data Engineer

Senior Data EngineerDirect Hire(Note: This position is not eligible for employment sponsorship now or in the future.)Location: Plano 75024Schedule: Remote; candidate needs to be located in DFW area or at minimum in TexasInterview process: 4 roundsSummaryWe are seeking a highly skilled Senior Data Engineer to help build, scale, and optimize a modern cloud-based data platform. This hands-on individual contributor role is responsible for designing, developing, and maintaining trusted data products that support analytics, reporting, business intelligence, machine learning, and future AI initiatives.The ideal candidate is a highly technical data professional who enjoys building modern data platforms, establishing trusted enterprise data assets, and enabling scalable analytics capabilities. This individual thrives in a collaborative environment, embraces ownership, and is passionate about leveraging data to drive business value and future AI innovation.Needs:Bachelor's degree in Computer Science, Data Engineering, Information Systems, Data Science, or a related field.Equivalent combination of education and relevant experience will be considered.7+ years of experience in Data Engineering, Analytics Engineering, Software Engineering, or related technical disciplines.Technical stack needed include Snowflake, Astronomer/Apache Airflow, FivetranExperience designing, building, and optimizing large-scale cloud-based data platforms.Proven experience supporting production data environments and operational support processes.Experience developing enterprise analytics, reporting, and self-service data solutions.Expert-level SQL development and query optimization skills.Extensive experience designing and implementing dimensional and semantic data models.Deep understanding of modern ELT/ETL architectures and analytics engineering principles.Experience with Git-based development workflows, version control, and code review processes.Strong Python programming skills for data processing, automation, APIs, and platform tooling.Experience developing reusable frameworks and operational utilities.Experience establishing business metrics, KPI definitions, and data standards.Strong understanding of metadata management, data lineage, stewardship, and data quality principles.Experience participating in enterprise data governance initiatives.Preferred: dbtGitHubRudderStackAtlan, Collibra, or similar governance platformsPower BI, Domo, Omni, Tableau, or similar business intelligence toolsAWSAmazon S3Amazon RDSAWS LambdaCloud storage, processing, and integration servicesOther needs:Strong ownership mentality with the ability to drive initiatives from concept through production.Exceptional analytical and problem-solving abilities.Passion for delivering trusted, high-quality data products.Ability to navigate ambiguity and solve complex business problems.Strong collaboration and stakeholder management skills.Excellent verbal and written communication skills.Continuous improvement mindset with a focus on innovation and operational excellence.Ability to balance technical rigor with business impact.Key ResponsibilitiesAnalytics Engineering & Data ModelingDesign, develop, and maintain scalable data transformation frameworks and models.Build and optimize dimensional, semantic, and analytics-focused data models for enterprise reporting and self-service analytics.Develop and maintain reusable, business-ready datasets and data products.Write, optimize, and maintain complex SQL transformations across large-scale datasets.Implement testing, documentation, lineage tracking, and monitoring to ensure data quality and reliability.Promote analytics engineering best practices and standards across the organization.Data Governance & Data ManagementPartner with business stakeholders to define, document, and maintain enterprise KPIs, metrics, and business definitions.Establish consistency across reporting, dashboards, operational analytics, and self-service reporting environments.Serve as the bridge between technical and business teams to ensure alignment on critical business concepts and reporting requirements.Support metadata management, data lineage, stewardship, ownership, and certification of trusted data assets.Champion data governance standards, naming conventions, documentation practices, and data quality initiatives.Assist in establishing scalable frameworks for managing enterprise data as a strategic asset.Data Reliability & Operational ExcellenceOwn data products and pipelines from design through deployment, monitoring, support, and continuous improvement.Implement automated data quality controls, validation frameworks, and observability standards.Define and monitor service-level expectations for critical data assets and processes.Investigate production issues, perform root-cause analysis, and implement corrective actions.Continuously improve platform performance, scalability, and operational efficiency.Python Development & AutomationDevelop Python-based tools and frameworks for automation, data quality validation, and platform operations.Build integrations with internal and external systems through APIs and automated workflows.Create solutions that improve productivity and reduce manual effort.Support troubleshooting, debugging, and optimization of production data pipelines and workflows.AI & Emerging Technology InitiativesPrepare enterprise data assets to support future AI, machine learning, and advanced analytics use cases.Explore opportunities to leverage AI-assisted development and analytics workflows.Support initiatives involving semantic models, retrieval-based architectures, intelligent automation, and AI-powered analytics.Evaluate emerging technologies and industry best practices within data engineering and analytics.Cross-Functional CollaborationPartner with stakeholders across Finance, Marketing, Operations, Supply Chain, Customer Experience, Digital, and other business functions.Translate business requirements into scalable data models and data products.Support analysts and business users by delivering reliable, accessible, and well-documented datasets.Communicate effectively with both technical and non-technical audiences.