Overview
Shift4 (NYSE: FOUR) is boldly redefining commerce by simplifying complex payments ecosystems across the world. As the leader in commerce-enabling technology, Shift4 powers billions of transactions annually for hundreds of thousands of businesses in virtually every industry. For more information, visit www.shift4.com.
Data Engineer, Principal
Summary
The Principal Data Engineer is responsible for leading the design, architecture, evolution, and maintenance of Shift4’s data warehouse and analytics data infrastructure at scale. This role focuses on owning complex data ingestion from source systems (primarily PostgreSQL), transforming and modeling data in the data warehouse (currently Snowflake), and ensuring the delivery of high-quality, performant, reporting-ready datasets for analytics and business intelligence needs.
This senior leadership position combines deep hands-on technical expertise with team guidance and strategic direction. The Principal Data Engineer partners closely with product, software engineering, analytics, and business stakeholders to translate complex requirements into scalable, reliable data solutions while mentoring and elevating the data engineering team.
Balance of Technical vs. Leadership
This role is designed to blend hands-on technical execution with team leadership. You will stay closely involved in architecture and critical implementations while guiding, mentoring, and helping scale the team.
Responsibilities
- Lead the design, development, and continuous optimization of scalable, reliable, and high-performance data warehouse architecture to support growing analytics and reporting needs.
- Own and evolve data ingestion pipelines that move operational data from source systems (primarily PostgreSQL) into the data warehouse (Snowflake) with exceptional reliability and data quality.
- Drive advanced data transformation and modeling initiatives to create reporting-friendly, analytics-optimized schemas and datasets.
- Write and optimize complex, high-performance SQL queries to power reporting services, dashboards, data-driven APIs, and business intelligence.
- Own and maintain a version-controlled database codebase (schemas, tables, views, transformations) using Git-based workflows.
- Ensure all data changes are properly promoted, tested, and validated across development, staging, and production environments.
- Collaborate with cross-functional stakeholders to define and execute data strategy and translate business requirements into scalable technical solutions.
- Establish, promote, and enforce data engineering best practices, including code quality, documentation, testing, observability, and performance standards.
- Monitor, troubleshoot, and proactively resolve data quality, latency, scalability, and reliability issues across the platform.
- Provide technical leadership and mentorship to data engineers, setting direction and elevating team capabilities.
- Contribute to architectural decisions and long-term roadmap planning for data modeling, ingestion patterns, warehouse optimization, and platform evolution.
- Participate in agile processes while delivering incremental, production-ready solutions that drive measurable business impact.
Success Milestones
- First 3 months: Ramp up on existing systems and infrastructure, build strong relationships with stakeholders, and identify quick wins and improvement opportunities.
- 6 months: Drive key technical initiatives, establish clear technical direction, and demonstrate measurable impact on team productivity, data reliability, and pipeline performance.
- 12 months: Fully own the data engineering strategy, deliver major roadmap items, and play a central role in scaling both systems and team capabilities.
Team Structure & Fit
The current data team consists of two Data Engineers and two QA resources. This Principal Data Engineer role serves as the technical lead, bridging execution and strategic direction while working closely with both technical contributors and cross-functional stakeholders. The team structure is fluid as we are actively expanding.
Immediate Priorities
Early focus will be on deeply understanding current data pipelines and infrastructure, addressing reliability and scalability gaps, and aligning on priorities with key stakeholders.
Roadmap & Key Initiatives
This role will play a central part in leading a defined data engineering roadmap, including modernizing data architecture, scaling pipelines for increased volume and complexity, enhancing data quality and observability, and supporting critical business analytics needs.
Qualifications
- 7–10+ years of professional experience in data engineering, analytics engineering, or backend/data-focused software development.
- Proven experience with large-scale or distributed data systems and platforms.
- Expert-level proficiency in SQL and writing complex, high-performance queries for analytical workloads.
- Strong understanding of data modeling concepts (dimensional modeling, star/snowflake schemas, analytics-optimized structures).
- Deep familiarity with ELT/ETL concepts, data pipelines, orchestration, and modern data stack practices.
- Hands-on experience ingesting and transforming data from relational databases, particularly PostgreSQL.
- Solid experience with Git-based version control for database code and data transformations.
- Demonstrated ability to support multiple environments (development, staging, production) with disciplined deployment and validation processes.
- Strong problem-solving skills with the ability to design scalable, maintainable, and resilient data solutions.
- Excellent communication skills and the ability to work independently while effectively engaging with both technical and non-technical stakeholders.
- Prior experience providing technical mentorship or leadership within a data or engineering team.
Preferred Qualifications
- Hands-on experience with Snowflake (or similar cloud data warehouses such as Redshift).
- Experience with data transformation frameworks (e.g., dbt or equivalent).
- Background building analytics-ready data layers for BI tools and reporting APIs.
- Strong understanding of data quality validation, monitoring, and observability practices.
- Experience working in cloud environments (AWS or similar).
- Familiarity with data security, encryption, governance, and compliance practices.
- Relevant professional certifications are a plus.
- Demonstrated ability to drive technical strategy and influence cross-functional teams.
We are an Equal Opportunity Employer and do not discriminate against any employee or applicant for employment because of race, color, sex, age, national origin, religion, sexual orientation, gender identity and/or expression, status as a veteran, and basis of disability or any other federal, state or local protected class.