Staff Data Engineer

About our Data Stack:

• Cloud Provider: AWS
• Database: MySQL, PostgreSQL
• Extract/Load: Fivetran
• Transform: dbt
• Data Warehouse: Snowflake
• BI Visualization: Looker
• Code versioning: Gitlab
• Preferred languages: SQL and Python
• Infrastructure as Code: Terraform
• Data Quality Monitoring: Monte Carlo, RDS Database Insights, and Datadog

Responsibilities:

As a Staff Data Engineer, you will provide technical leadership in the design, development, and evolution of PayNearMe's modern data platform and data products. You will work closely with Product, Engineering, Risk, Finance, Operations, and Analytics teams to model complex payment data into trusted, governed, and reusable data products that power reporting, analytics, operational intelligence, AI, and customer-facing applications.

This role requires deep expertise in enterprise data modeling, modern cloud data engineering, and payment data. You'll lead the design of scalable data models across the Bronze, Silver, and Gold layers of our lakehouse architecture while establishing engineering best practices and mentoring other engineers through technical leadership..

Enterprise Data Modeling

• Lead the design and implementation of scalable data models across Bronze, Silver, and Gold layers
  Collaborate with business stakeholders, product managers, and engineering teams to understand business processes and translate them into well-designed analytical data models.
• Design conceptual, logical, and physical data models supporting enterprise reporting, operational analytics, and AI initiatives
• Develop reusable semantic models that provide consistent business definitions and metrics across the organization
• Apply dimensional modeling best practices including:
  • Fact and dimension modeling
  • Star and snowflake schemas
  • Slowly Changing Dimensions (SCD)
  • Conformed dimensions
  • Semantic layer design
• Ensure data models are scalable, maintainable, performant, and easily consumable

Data Engineering & Data Products

• Design, build, and optimize cloud-native ELT pipelines using dbt, Fivetran, Python, and AWS Airflow
• Build trusted, reusable data products supporting:
  • Payment and transaction analytics
  • Merchant reporting
  • Customer insights
  • Financial reporting
  • Fraud and risk analytics
• Regulatory reporting
• Capture and transform transactional payment data from operational systems into curated analytical datasets
• Design highly performant incremental data pipelines capable of processing high-volume payment transactions
• Optimize query performance and execution

Payment Data Expertise

• Develop a deep understanding of PayNearMe's payment ecosystem, including payment lifecycle events, settlements, ACH, card processing, client operations, and consumer transactions
• Model complex financial and payment data with a focus on accuracy, reconciliation, auditability, and regulatory compliance
• Partner with domain experts to establish trusted enterprise definitions and business metrics

Technical Leadership

• Serve as a technical leader and trusted advisor across Data Product Engineering initiatives
• Drive engineering standards, reusable design patterns, and best practices for data modeling and pipeline development
• Participate in architecture reviews and influence technical direction across multiple engineering teams.
• Mentor engineers through design reviews, code reviews, documentation, and technical coaching
• Promote engineering excellence through testing, CI/CD, observability, and Infrastructure-as-Code practices

Cross-Functional Collaboration

• Partner closely with Product, Engineering, Data Science, Analytics, Finance, Risk, and Operations teams to deliver high-value data products
• Work collaboratively with stakeholders to understand evolving business requirements and translate them into scalable technical solutions
• Communicate complex technical concepts clearly to both technical and non-technical audiences
• Foster strong collaboration across teams to improve data quality, governance, and business alignment

Qualifications

• Bachelor's degree in Computer Science, Information Systems, Engineering, Mathematics, Statistics, or related field.
• 10+ years of professional experience in Data Engineering, Analytics Engineering, or Data Platform Engineering.
• Deep expertise designing enterprise-scale data platforms and cloud-native data architectures.
• Strong experience modeling complex business domains with emphasis on payment, financial, or transactional data.
• Expert knowledge of:
  • Dimensional modeling
  • Fact and dimension design
  • Star schema design
  • Semantic modeling
  • Data warehouse architecture
• Experience designing and maintaining curated Silver and Gold layer data models within modern lakehouse architectures.
• Expert SQL skills with demonstrated experience writing highly performant analytical queries.
• Strong Python programming skills.
• Extensive hands-on experience with:
  • Snowflake
  • dbt
  • Fivetran
  • AWS
  • GitLab
  • Looker / LookML
• Strong understanding of:
  • ELT architectures
  • Data quality
  • Metadata management
  • Data lineage
  • Data observability
  • CI/CD pipelines
  • Infrastructure-as-Code
• Experience building highly reliable, scalable, and maintainable data pipelines.
• Excellent problem-solving and diagnostic skills.
• Exceptional written and verbal communication skills.
• Demonstrated ability to influence technical direction without direct management responsibility.
• Strong organizational and communication skills

Preferred Qualifications

• Experience within fintech, payment processing, banking, or regulated financial services.
• Experience with Apache Iceberg and modern lakehouse architectures.
• Familiarity with Dataiku, Monte Carlo, and Terraform/OpenTofu.
• Experience designing data products supporting AI and machine learning initiatives.
• Knowledge of PCI DSS, SOC 2, and financial data governance requirements.
• Experience implementing semantic layers and enterprise business metrics.
• Experience mentoring engineers and driving engineering standards across teams.