Senior Staff Software 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.