Remote Ai Data Engineer Jobs (NOW HIRING)

Order.co is the System of Action for the Office of the CFO, transforming the way businesses purchase and pay into an intuitive, B2C-like shopping experience. Order.co leverages embedded AI agents and embedded financial products to reinvent the way businesses connect with their vendors.
End users enjoy a seamless, zero-training buying experience, while finance and procurement leaders gain a single platform to orchestrate how the business "should operate". The result is an all-in-one solution that serves as a gravitational pull for spend and data, automating and eliminating procurement and finance workflows from requisition to reconciliation along the way.
Order.co is on the cutting edge of B2B Agentic Commerce, poised to be the market leader in creating a more predictive, prescriptive, and personalized experience for users.
Founded in 2016 and headquartered in New York City, Order.co oversees nearly half a billion in annualized spend across hundreds of customers like WeWork, SoulCycle, Lume, and [solidcore]. Order.co has raised $75M in funding from industry-leading investors like MIT, Stage 2 Capital, Rally Ventures, 645 Ventures, and more. Order.co has been proudly named a 50 to Watch by Spend Matters and a Best Place to Work by BuiltIn and Inc. Magazine.
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.
Responsibilities
Data & 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:
Failure Mode
What It Looks Like
Strong coder, weak owner
Ships code but doesn't manage to the task - owns the merge, not the outcome; hands off and moves on without monitoring or fixing post-release issues
Solo expert
Hoards knowledge instead of sharing - becomes a single point of failure and blocks team growth
Overconfident designer
Proposes solutions without considering trade-offs - jumps to conclusions, resists alternative approaches
Rubber-stamper
Produces AI-generated output without verifying it against the codebase, tests, or business context
Interview Process
Our 5-round process is designed to evaluate you across all competency areas. AI tools are permitted in technical rounds.
Round
Format
What We Evaluate
1 - Hiring Manager Screen
60 min, conversational
Career trajectory, mentorship philosophy, technical influence examples, communication style
2 - Take-Home + PR Discussion
72h take-home + 60 min live
Navigating unfamiliar code, ownership and decomposition discipline visible in your PR, root-cause judgment, AI tool usage
3 - System Design + Artifact Critique
60 min, Miro board
Requirements gathering, schema/API design, trade-off articulation, calibrated code-review judgment on a teammate's PR
4 - Team Interview (conditional)
30 min, behavioral
Collaboration patterns, mentorship behavior, negotiation behavior with cross-functional partners
5 - Culture Add
30 min, People Team
Organizational values alignment
Round 4 is conditional: it runs when the team needs additional behavioral signal after Rounds 2 and 3, and is otherwise skipped. Your recruiter will tell you whether it's scheduled before your loop is finalized.
The Round 2 (Take-Home + PR Discussion) and Round 3 (System Design) exercises are drawn from real problems so the technical evaluation is grounded in the work you'd actually be doing.
What You'll Receive

• Competitive compensation including base salary, bonus, and equity
• Employer-sponsored 401(k) with match
• Comprehensive medical, dental, and vision coverage
• Flexible time off and hybrid work environment

The anticipated annual salary range for this role is $175,000 - $200,000. Actual compensation and title will be commensurate with experience, qualifications, knowledge, and skills.