Staff Software Engineer - Data Infrastructure

Plaid2 weeks ago

Location

San Francisco HQ

Type

Full Time

Salary

USD 207,600 – 273,600

Level

Staff

Role

Data Engineer

Posted

Jul 6, 2026

Full TimeStaff

The role

Summary

Staff Software Engineer - Data Infrastructure at Plaid focuses on architecting and scaling data warehouse, data lakehouse, and streaming infrastructure systems. You'll own key technical initiatives to enable machine learning, ETL pipelines, and data platform abstractions while mentoring engineers across the organization. This role requires 6+ years of hands-on software engineering expertise in data infrastructure systems, along with demonstrated leadership capabilities and cross-functional collaboration skills.

What you'll do

Technical Roadmap Leadership: Contribute to and guide the long-term technical roadmap for data-driven and machine learning iteration at Plaid, ensuring alignment with business objectives and technical excellence. Establish best practices and architectural patterns for data infrastructure systems.

Key Data Infrastructure Project Ownership: Lead critical data infrastructure initiatives including ML development golden paths, offline streaming solutions for data freshness, new ETL pipeline infrastructure development, and evolution of data warehouse and data lakehouse capabilities. Make architectural decisions that impact platform scalability and performance.

Cross-Functional Stakeholder Collaboration: Work closely with engineering, product, and business teams to define technical roadmaps for backend systems and data abstractions. Communicate complex technical decisions to non-technical stakeholders and gather requirements from diverse team functions.

Platform Operations and Reliability: Debug, troubleshoot, and reduce operational burden on the Data Platform. Identify performance bottlenecks, implement cost optimization strategies, and ensure platform reliability and data quality across the organization.

Team Growth and Mentorship: Mentor and develop junior and mid-level engineers through code reviews, technical document reviews, and pair programming sessions. Establish engineering standards, conduct architectural reviews, and guide team members on system design best practices and career development.

What we look for

Technical

Data Infrastructure Systems ArchitectureExpert-level knowledge of data warehouse, data lakehouse, and distributed data systems architecture. Deep understanding of performance tuning, cost optimization, and scalability patterns in large-scale data platforms.

Apache Spark and Distributed ComputingExtensive hands-on experience with Apache Spark, including RDD, DataFrame, and SQL APIs. Proficiency in optimizing Spark jobs, managing memory, and debugging distributed computing challenges in production environments.

Workflow Orchestration and SchedulingStrong experience designing, building, and maintaining workflow orchestration systems. Understanding of distributed scheduling, fault tolerance, monitoring, and SLA management in data pipelines.

Streaming Data InfrastructureHands-on experience building and maintaining streaming data systems for real-time data processing. Knowledge of event-driven architectures, exactly-once semantics, backpressure handling, and streaming framework trade-offs.

Coding and System DesignAdvanced proficiency in software engineering fundamentals including design patterns, testing strategies, code organization, and API design. Proven ability to write scalable, maintainable code with comprehensive testing coverage.

Education

Bachelor's Degree in Computer Science or Related FieldFormal education in computer science, software engineering, or related discipline. Equivalent professional experience can substitute for formal education requirements.

Experience

Software Engineering Experience6+ years of professional software engineering experience with demonstrated expertise in building and scaling production systems. Track record of successful project delivery and technical decision-making at scale.

Data Infrastructure Domain ExperienceSubstantial hands-on experience in data infrastructure or platform engineering roles at mid to large-sized technology companies. Proven success designing and implementing systems that thousands of engineers or teams depend upon.

Leadership and MentorshipDemonstrated experience mentoring junior and mid-level engineers, conducting code reviews, and driving technical decisions. Ability to influence across teams and establish engineering standards and best practices.

Cross-Functional CollaborationProven ability to work effectively with product managers, stakeholders, and engineers across multiple teams. Experience gathering requirements, communicating technical constraints, and delivering solutions that balance technical and business needs.

Skills

Required skills

Apache SparkProduction-level proficiency in Apache Spark for large-scale distributed data processing, including performance optimization and troubleshooting.

Data Warehouse DesignStrong understanding of data warehouse architecture, schema design, query optimization, and analytics workload patterns. Experience with modern cloud data warehouses.

Workflow OrchestrationHands-on experience building data pipelines with orchestration tools and managing complex ETL workflows at scale.

PythonStrong Python programming skills for building data infrastructure tools, utilities, and backend systems.

System DesignAdvanced system design skills including scalability analysis, performance optimization, and architectural decision-making for data platforms.

SQLExpert-level SQL proficiency for query writing, optimization, and complex data analysis.

Nice to have

Databricks PlatformProduction experience with Databricks unified analytics platform, including Delta Lake, MLflow, and managed workflows.

Apache AirflowHands-on experience with Apache Airflow for workflow orchestration, DAG design, and operational management.

AWS EMR and Cloud Data InfrastructureExperience deploying and managing Hadoop/Spark clusters on AWS EMR or building cloud-native data infrastructure on AWS.

Streaming TechnologiesProduction experience with Apache Kafka, Apache Flink, or similar streaming platforms for real-time data processing.

Data Lakehouse ArchitectureHands-on experience building or maintaining data lakehouse systems combining data lake and warehouse capabilities.

ML Platform EngineeringExperience building infrastructure supporting machine learning workflows, feature stores, or MLOps platforms.

Compensation & benefits

Salary

USD 207,600 – 273,600 (annual)

Stock options

Available

Benefits

Comprehensive Medical, Dental, and Vision Coverage

Full health insurance coverage including medical, dental, and vision plans with employer contributions.

401(k) Retirement Plan

Tax-advantaged retirement savings plan with company match eligibility.

Equity and Stock Options

Competitive equity grants or stock options as part of comprehensive compensation package for staff-level engineers.

Professional Development and Learning

Access to learning resources, conference attendance budgets, and professional development opportunities to advance technical expertise.

Flexible Work Arrangement

Work environment supporting flexible schedules and collaboration across Plaid's offices in San Francisco, New York, Washington D.C., London, and Amsterdam.

Mentorship and Career Growth

Structured mentorship programs, technical leadership opportunities, and clear career advancement paths for staff engineers.

Interview process

1
Application Review and Initial Screening — Plaid's recruiting team reviews your application, focusing on relevant data infrastructure experience, technical depth, and alignment with staff-level expectations. Prior experience at scaled companies with large data systems is particularly valuable.
2
Technical Phone Screen — Initial 45-60 minute conversation with a data infrastructure engineer covering your background, key projects, system design approaches, and technical decision-making philosophy. Expect questions about distributed systems, data pipeline architecture, and your experience with relevant technologies.
3
System Design Interview — In-depth technical interview focused on designing data infrastructure systems at scale. You'll discuss tradeoffs in warehouse vs. lakehouse architectures, workflow orchestration patterns, streaming data handling, and cost optimization strategies. Emphasis on your ability to think through complex architectural decisions.
4
Technical Deep Dive — Detailed conversation about your most significant data infrastructure projects, including challenges faced, solutions designed, and lessons learned. Expect technical depth probing your expertise in Spark, workflow orchestration, or streaming technologies.
5
Leadership and Impact Assessment — Behavioral interview with senior engineering leadership evaluating your mentorship approach, cross-functional collaboration skills, and track record of delivering large technical initiatives. Discussion of how you've influenced technical roadmaps and grown engineering teams.
6
Leadership and Hiring Manager Interview — Final conversation with the Data Infrastructure team lead or director covering team dynamics, long-term vision for the platform, your leadership style, and how you approach mentoring and building high-performing teams.