Staff Data Engineer

Priority Technology Holdings, LLC• Remote, US,

3d•$145,000 - $190,000•Remote

About The Position

The Staff Data Engineer is a senior individual contributor responsible for designing and evolving scalable data architecture across Priority Holdings’ payments and commerce ecosystem. This role provides technical leadership across data ingestion, processing, Snowflake platform capabilities, data delivery, and data products, while helping establish engineering standards and reusable patterns across teams. You will work on complex, cross-domain problems that require strong technical judgment, influence architecture and platform direction, and enable multiple engineering teams to deliver reliable, secure, and cost-effective data solutions. The role also provides an opportunity to shape emerging capabilities in AI, automation, and MCP-enabled data access as the organization expands its data platform strategy.

Requirements

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.

Nice To Haves

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.

Responsibilities

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.