Senior AI / Data Engineer

The Role
As a Senior AI / Data Engineer, you will design, build, and maintain scalable data and AI infrastructure that powers critical business and product capabilities across the organization. You will partner closely with software engineers, analytics, product, and operations teams to deliver reliable data systems, production-ready AI workflows, and high-quality datasets that enable intelligent decision-making and automation.

You bring strong software engineering fundamentals together with practical expertise in data systems, cloud infrastructure, and AI-enabled development workflows. You care deeply about reliability, maintainability, operational excellence, security and building systems that scale with the business.

ResponsibilitiesData & AI Platform Engineering
Design, build, and maintain scalable data pipelines, integrations, and AI workflows
Develop reliable and maintainable ETL/ELT systems that support analytics, operational reporting, and AI-driven products
Contribute to the architecture and evolution of the company’s data platform and AI infrastructure
Build systems and services with a focus on simplicity, iterative development, reliability, and long-term maintainability
Continuously optimize data architecture to support evolving business and product requirements
Partner with stakeholders to translate business problems into scalable data and AI solutions
Infrastructure, Automation & Reliability
Develop infrastructure automation and deployment workflows to improve engineering velocity and operational consistency
Implement infrastructure as code (IaC) practices using tools such as Terraform or CloudFormation
Build and maintain CI/CD pipelines and automated testing workflows
Develop monitoring, alerting, and observability solutions for data and AI systems
Improve reliability, scalability, and operational efficiency through automation and proactive system improvements
Participate in incident response and operational support rotations as needed
AI Enablement & Engineering Productivity
Contribute to production-ready AI systems and workflows where they provide measurable business value
Evaluate and integrate AI-assisted engineering tools responsibly and pragmatically
Support the deployment and operationalization of machine learning and AI-powered services
Help establish best practices for AI-assisted software development, evaluation, and operational safety
Collaboration & Technical Leadership
Contribute to roadmap planning, technical design discussions, and engineering prioritization
Mentor junior and mid-level engineers through code reviews, pairing, and technical guidance
Collaborate cross-functionally with Engineering, Product, Analytics, and Operations teams
Communicate technical trade-offs, implementation details, and operational risks clearly to stakeholders
Promote engineering best practices around testing, observability, documentation, and operational excellence
Qualifications
You are motivated by accountability and ownership of outcomes
You are results-oriented and focused on delivering reliable, working systems
Writing tests is an integral part of your development process
You know how to design and build software incrementally
You enjoy collaborating with others to solve complex technical problems
You are collaborative, open-minded, and continuously improving your craft
You are curious and pragmatic about AI-driven solutions and apply them thoughtfully where they create real value
You understand both the strengths and limitations of AI-assisted engineering tools and evaluate their output critically
Technical Skills
Strong proficiency in Python and SQL
Hands-on experience with data orchestration tools (preferably Airflow, Dagster, or AWS Step Functions)
Proven experience building and operating AWS cloud infrastructure, particularly services such as Lambda, ECS, and SQS
Experience implementing infrastructure as code using Terraform or similar tooling
Strong experience designing event-driven, serverless architectures using AWS Lambda, API Gateway, EventBridge, and SQS/SNS
Hands-on experience working with large-scale data platforms in production environments (preferably Spark/PySpark, AWS Glue, or EMR)
Strong understanding of AWS data lake technologies including S3, Glue Catalog, and Lake Formation
Hands-on experience with cloud data warehouses (preferably Snowflake) including schema design, performance tuning, cost optimization, and access control
Experience designing and maintaining reliable ETL/ELT pipelines and distributed data workflows
Hands-on experience with SQL-based transformation frameworks such as dbt (Core or Cloud)
Familiarity with CI/CD systems and tooling such as GitHub Actions or CircleCI
Understanding of observability, monitoring, and operational best practices for data systems
Strong understanding of data security, access controls, and protecting sensitive data
Experience building automation and operational tooling using Python or similar languages
Familiarity with production AI/ML workflows and operational considerations for AI-enabled systems
Experience using AI-assisted engineering tools (e.g., Claude Code, Codex, GitHub Copilot) responsibly to improve productivity and engineering quality
What Great Looks Like
A Senior Software Engineer on the Payments team who is thriving at this level demonstrates:

Reliable delivery of complex work — consistently ships multi-part solutions on time with low defect rates
Low defects in owned areas — proactively monitors and improves the quality of the systems they own; that means incident-free quarters in code paths that move funds and clean reconciliation against vendor reports
Measurable mentorship impact — engineers around you write better code because of your reviews and guidance

"Someone we can depend on for the work that matters — especially the work that touches money."

Failure Modes We Screen Against
We actively evaluate candidates for the following anti-patterns during the interview process: