About the Role
The Staff Data Engineer will provide technical leadership in shaping the data architecture and engineering practices, working cross-functionally to model complex payment data into trusted, governed data assets used for reporting, analytics, risk, and operational intelligence.
Responsibilities
- Lead technical strategy for the design and evolution of the company's cloud data platform and data product ecosystem.
- Collaborate with product, engineering, analytics, risk, finance, and operations teams to transform complex business processes into reliable data models.
- Apply deep knowledge of enterprise data modeling, cloud data engineering, and financial transaction systems to solve scalable data challenges.
- Guide the development of data models across raw, refined, and curated layers of the lakehouse architecture.
- Define and implement scalable data models in alignment with business needs across multiple domains.
- Work with stakeholders to translate business logic into conceptual, logical, and physical data models for analytics and reporting.
- Create reusable semantic models that standardize key business metrics and definitions enterprise-wide.
- Use dimensional modeling techniques such as fact and dimension tables, star and snowflake schemas, slowly changing dimensions, and conformed dimensions.
- Ensure all data models are maintainable, high-performing, and easy to consume by downstream systems.
- Design, build, and maintain cloud-based ELT pipelines using dbt, Fivetran, Python, and AWS Airflow.
- Develop data products that support transaction analysis, merchant reporting, customer insights, fraud detection, and compliance.
- Extract and transform payment data from operational systems into clean, analytics-ready datasets.
- Build efficient, incremental data pipelines capable of handling high-throughput transaction volumes.
- Improve query efficiency and pipeline performance across large datasets.
- Gain in-depth knowledge of the payment ecosystem, including transaction lifecycles, settlements, ACH, card processing, and client interactions.
- Model financial and payment data with emphasis on accuracy, auditability, reconciliation, and compliance.
- Work with domain specialists to define and standardize critical business metrics and data definitions.
- Act as a technical authority and advisor on data product engineering initiatives.
- Establish engineering standards, reusable patterns, and best practices for data modeling and pipeline development.
- Contribute to architecture reviews and help shape technical direction across engineering teams.
- Mentor data engineers through code reviews, design sessions, documentation, and technical guidance.
- Champion software engineering excellence through testing, CI/CD, observability, and Infrastructure-as-Code.
- Collaborate with cross-functional teams to deliver high-impact data solutions.
- Translate evolving business needs into scalable, future-proof technical designs.
- Clearly communicate technical concepts to both technical and non-technical audiences.
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
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
