Senior Staff Data Engineer

Staff / Senior Technical Individual Contributor, Scottsdale, Arizona Sponsorship: Not eligible for sponsorship We are seeking a highly experienced Staff Data Engineer to join a growing Data Engineering team. This is the most senior technical individual contributor role on the team and is ideal for a hands-on engineer who combines deep software engineering expertise with enterprise data architecture, cloud engineering, data governance, and AI/GenAI capabilities. The Staff Data Engineer will define and evolve long-term data architecture and technical standards across teams and platforms while remaining hands-on with coding and engineering. This individual will serve as a technical authority, mentor senior engineers, lead complex cross-team initiatives, and make critical architectural decisions that improve scalability, reliability, cost efficiency, and maintainability. Key Responsibilities Define and evolve long-term enterprise data architecture, technical standards, patterns, and best practices across teams and platforms.Remain hands-on with software development and data engineering, with strong expertise in Python, SQL, Spark, and AWS.Design and oversee highly scalable, resilient, fault-tolerant, secure, and cost-efficient data systems using AWS cloud technologies.Lead complex cross-team and multi-system data initiatives spanning multiple business functions, platforms, and data domains.Serve as the senior technical authority and escalation point for complex data engineering, architecture, and production challenges.Make principled architectural tradeoffs between AWS-managed services and open technologies, such as Apache Spark, Flink, and Iceberg, based on scalability, maintainability, performance, and cost.Establish and evolve enterprise standards for data quality, reliability, observability, security, governance, and operational excellence.Drive alignment on data modeling, data warehousing, batch processing, real-time/streaming integration, and platform usage patterns.Identify and reduce technical debt, duplication, operational risks, and architectural inconsistencies across data platforms.Partner with engineering, analytics, architecture, product, and business leaders to translate strategic objectives into scalable technical data solutions.Design and implement highly reliable data pipelines and distributed systems capable of supporting mission-critical production workloads.Apply strong knowledge of distributed systems, partitioning, performance optimization, fault tolerance, and scalability to solve complex engineering problems.Use an AI-first approach to improve engineering productivity, automation, operational efficiency, data quality, and systemic risk management.Apply AI/GenAI capabilities in production engineering environments, including automation, developer productivity, data quality, or operational workflows.Mentor senior and experienced engineers through architecture discussions, code/design reviews, technical guidance, and knowledge sharing.Influence technical direction across teams without direct people-management responsibility.Balance near-term delivery priorities with long-term platform health, scalability, sustainability, and technical excellence.Establish best practices around CI/CD, infrastructure-as-code, automation, monitoring, observability, and production reliability.Lead root-cause analysis and resolution of complex issues across data pipelines, distributed systems, and cloud infrastructure. Required Qualifications Bachelor's degree in Computer Science, Information Systems, Computer Engineering, or a related field, or equivalent practical experience.8+ years of experience in data engineering, software engineering, platform engineering, or a closely related field.Proven experience designing and evolving large-scale, cloud-based data platforms, particularly in AWS.Strong hands-on programming experience with Python, SQL, and Spark.Expert-level understanding of AWS data services and cloud data architecture.Strong experience with data engineering, data architecture, data governance, and data warehousing.Demonstrated experience leading cross-team, multi-system data initiatives with enterprise-wide architectural impact.Experience owning or supporting mission-critical production data systems.Deep understanding of distributed systems, data partitioning, performance tuning, scalability, and fault tolerance.Advanced expertise in data modeling and data warehouse architecture.Experience designing scalable, resilient, observable, secure, and cost-efficient data platforms.Strong knowledge of data quality, observability, reliability, security, governance, and compliance best practices.Experience with Infrastructure-as-Code, CI/CD, and engineering automation.Strong debugging, troubleshooting, and root-cause analysis capabilities across data pipelines and cloud infrastructure.Proven ability to influence architecture and technical direction across multiple teams without formal people-management responsibility.Demonstrated experience mentoring senior engineers and serving as a technical authority.Hands-on experience applying AI/GenAI in production to engineering workflows, automation, data quality, operational efficiency, or developer productivity. Preferred Technical Experience AWS: S3, Glue, EMR, Lambda, Redshift, Kinesis, Athena, Step Functions, CloudWatch, or comparable services.Data Processing: Apache Spark, PySpark, Flink.Modern Data Technologies: Apache Iceberg or comparable open table formats.Data Architecture: Lakehouse, data lake, data warehouse, batch and real-time/streaming architectures.Programming: Python, SQL, Scala, or comparable languages.DevOps: Terraform/CloudFormation, Git, Jenkins, GitHub Actions, or comparable CI/CD technologies.Experience implementing enterprise-wide data governance and data quality standards.Experience applying GenAI/LLMs to production engineering or data engineering workflows.