Principal Data Engineer
Shift4
full-time
Posted on:
Location Type: Remote
Location: United States
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Job Level
About the role
- 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.
Requirements
- 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.
Benefits
- Health insurance
- 401(k) matching
- Flexible work hours
- Paid time off
- Professional development opportunities
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
data engineeringanalytics engineeringbackend software developmentSQLdata modelingELTETLdata pipelinesdata transformationdata ingestion
Soft Skills
problem-solvingcommunicationtechnical mentorshipleadershipcollaborationindependent workstakeholder engagementcode qualitydocumentationtesting