Staff Data Engineer

Priority$145K – $190K per yearUnited States only

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Job title: Staff Data Engineer
Reports to: Director, Data Engineering
Department: Central Services
Location: Remote

About Priority : Priority Commerce is a leading financial technology company on a mission to deliver a personalized, easy-to-adopt financial toolset that accelerates cash flow and optimizes working capital for businesses. Our vision is to eliminate the barriers to unlocking revenue - empowering businesses to grow faster and operate smarter.

We achieve this through the Priority Commerce Engine, an innovative platform that combines payables, acquiring, and banking and treasury solutions. This unified approach allows businesses to streamline financial operations, reduce unnecessary costs, and uncover new revenue opportunities.

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Staff Data Engineer at Priority · ApplyPick

Leads the technical design of large-scale data systems, pipelines, and platform capabilities across multiple domains and engineering teams.
Defines reusable architecture patterns, engineering standards, and best practices for ingestion, transformation, orchestration, data quality, and data delivery.
Designs scalable solutions across Snowflake and supporting cloud technologies with a focus on reliability, performance, maintainability, and cost efficiency.
Partners with Data Engineering, Analytics Engineering, QA, Software Engineering, and business teams to translate complex requirements into sustainable technical solutions.
Provides technical leadership for internal and external data ingestion patterns, including batch, CDC, streaming, APIs, and file-based integrations.
Drives architecture for outbound data delivery through data sharing, APIs, SFTP, cross-cloud integrations, and other distribution mechanisms.
Supports the evolution of data products such as Reporting Platform, DataBridge, AI-enabled applications, and MCP-based data services.
Establishes and improves engineering practices for observability, monitoring, automated testing, data quality, CI/CD, and operational readiness.
Identifies platform limitations, technical debt, and scalability risks, and drives long-term solutions rather than tactical fixes.
Leads technical reviews for high-impact initiatives and ensures architectural decisions are aligned with enterprise standards and long-term platform direction.
Mentors senior and junior engineers through design reviews, technical guidance, and knowledge sharing.
Partners with engineering and business leadership on roadmap planning, architecture decisions, technical tradeoffs, and investment priorities.
Supports complex production incidents and drives root-cause resolution, prevention, and improvements to platform reliability.
Evaluates emerging technologies and introduces new approaches when they provide measurable improvements in scalability, automation, developer productivity, or business capability.
What Success Looks Like: •Complex data initiatives are delivered using scalable, reusable, and well-governed architectural patterns.
Engineering teams benefit from clearer standards, stronger technical guidance, and reduced duplication across implementations.
Data pipelines and platform capabilities demonstrate improved reliability, observability, scalability, and operational readiness.
Technical decisions balance immediate delivery requirements with long-term platform sustainability and cost efficiency.
Cross-team architecture issues and ambiguous technical problems are resolved with clear direction and sound engineering judgment.
Senior and junior engineers become more effective through mentorship, design guidance, and reusable frameworks.
Data products and downstream consumers receive trusted, timely, and well-supported data through standardized platform capabilities.
Platform improvements reduce manual effort, recurring incidents, and technical debt.
The role is viewed as a trusted technical partner by engineering teams, leadership, and cross-functional stakeholders.
New capabilities in AI, automation, and MCP are introduced with appropriate security, governance, reliability, and architectural discipline.

Required
8 ~ 12 years of experience in data engineering, software engineering, data platform engineering, or a related technical field.
Deep expertise in data architecture, distributed data processing, data warehousing, and large-scale data platform design.
Strong hands-on experience with Snowflake, including architecture, performance optimization, security, governance, and cost management.
Strong data querying skills and coding proficiency in Python, Java, or similar programming languages.
Strong experience with data modeling, ETL/ELT architecture, workflow orchestration, and performance optimization.
Experience designing and operating production-grade batch, streaming, or CDC-based data pipelines.
Strong experience with cloud-based data platforms and modern data engineering architectures.
Demonstrated ability to design scalable technical solutions across multiple systems and domains.
Experience establishing engineering standards, reusable frameworks, or architecture patterns adopted by multiple teams.
Strong understanding of data quality, observability, monitoring, CI/CD, and operational reliability.
Demonstrated ability to operate independently on highly ambiguous and complex technical problems.
Experience mentoring engineers and influencing technical decisions without direct management authority.
Strong communication skills with the ability to explain complex technical concepts, tradeoffs, and recommendations to technical and non-technical stakeholders.
Bachelor’s degree in Computer Science, Engineering, Information Systems, Data Science, or a related field, or equivalent practical experience.
Preferred
Experience with AWS data services, distributed processing technologies, and event-driven architectures.
Experience with technologies such as Kafka, Debezium, Spark/PySpark, Airflow, or similar platforms.
Experience designing external data sharing and delivery solutions across Snowflake, APIs, SFTP, and cross-cloud environments.
Experience with data platforms supporting fintech, payments, commerce, financial services, or other highly regulated environments.
Familiarity with AI/ML data architectures, AI-enabled applications, agentic workflows, or MCP-based integrations.
Experience designing platforms or frameworks that enable self-service data engineering or workflow automation.
Experience with governance, lineage, metadata management, access controls, and secure data-sharing patterns.
Experience leading major modernization, migration, or platform transformation initiatives.
Master's degree in Computer Science, Engineering, Data Science, or a related technical discipline.

Staff Data Engineer

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Staff Data Engineer

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