Sr. Data Engineer

Ursa Space Systems is seeking a seasoned Senior Data Engineer to architect and enhance their primary data infrastructure. The role involves creating dependable data pipelines, overseeing cloud-based data warehouses, and ensuring high-availability access to data for machine learning and analytics units.

Responsibilities:

• Define and evolve the data engineering technology roadmaps aligned to business strategies, enterprise architecture, information security standards
• Provide architectural leadership on complex, cross-team data initiatives, ensuring solutions are scalable, secure, resilient, and maintainable
• Lead data platform and vendor evaluations, guiding build vs. buy decisions and ensuring consistency with enterprise technical direction
• Develop and promote data engineering standards for lakehouse architecture, ELT/ETL frameworks, data modeling, CI/CD, observability, and governance
• Anticipate technology impacts across applications, data, integrations, and infrastructure—and guide teams through informed trade-off decisions including assessing new technology
• Day-to-day hands-on execution and implementation of solutions - using modern data platforms, initiatives, processes and practices
• Be responsible for technical direction for large or multi-team delivery efforts, ensuring application of modern data engineering and DevSecOps practices
• Conduct architectural and technical reviews of solution designs, code, and delivery approaches to raise quality and consistency
• Champion automation, observability, and operational excellence to ensure platforms remain reliable, performant, and cost effective
• Pilot emerging technologies and guide their adoption into stable enterprise capabilities when appropriate
• Drive continuous improvement of engineering practices, tooling, automation and operational processes
• Based on the needs of the company, you may be required to work occasional nights and weekends
• All other duties as assigned

Requirements:

• Bachelor's degree or equivalent experience typically requires an advanced degree or equivalent experience and a minimum of 10+ years of relevant data engineering experience in enterprise environments
• Mastery-level knowledge of the data engineering field and modern data platform technology delivery, with deep experience in one or more key domains such as lakehouse architecture, distributed data processing, cloud data warehousing, data transformation frameworks, or data governance
• Demonstrated ability to build and complete implementation plans for complex, enterprise-scale data platforms and pipelines. These platforms support analytics, reporting, AI/ML, and operational decision-making. They also impact on the achievement of the foundation's priorities
• Proven experience developing and implementing standards, processes, and operational plans that improve stability, resilience, security, and performance of critical data platforms
• Deep expertise with cloud-based data platforms - Azure Data services, Databricks, Snowflake and DBT cloud
• Strong foundation in enterprise data architecture fields and the development of secure, scalable, aligned technology solutions
• Advanced experience with CI/CD pipelines for data workloads, infrastructure as code, cloud-native operational models, and modern version control strategies
• Strong proficiency in programming using data processing tools like Azure Data Factory, Fivetran, Databricks notebook, DBT cloud, ETL/ELT frameworks, as well as programming languages such as Python, SQL, Spark (PySpark/Scala) and scripting languages common to cloud data environments
• Demonstrated success leading large-scale and complex data modernization initiatives, including migration from legacy data warehouses to modern cloud-based data platforms
• Experience managing and reviewing vendors or offshore engineering work to ensure alignment with enterprise data standards, architecture principles and long-term sustainability
• Excellent communication and influencing skills, with the ability to clearly and succinctly translate complex data architecture concepts into actionable recommendations for senior leadership, technical teams, and non-technical partners across multiple functions
• Demonstrated ability to enable AI through data, with a strong understanding of AI use cases and the data architecture, quality, governance, lineage, and scalability requirements necessary to successfully support AI initiatives
• Strong analytical and problem-solving abilities, demonstrating a dedication to operational excellence, continuous learning, and improving data engineering practices
• May mentor, review, and delegate work to more junior engineers, building capability and maturity across the data engineering group