# Senior AI / Data Engineer

**Company:** [Order.co](https://hotfix.jobs/companies/orderco)
**Location:** Remote
**Role:** Data Engineering
**Experience:** 5+ years
**Skills:** Python, SQL, Airflow, Dagster, AWS Lambda, Aws Ecs, Aws Sqs, Terraform, Aws Api Gateway, Eventbridge, Spark, Pyspark, Aws Glue, Emr, S3
**Posted:** 2026-05-26

> Design, build, and maintain scalable data pipelines and AI infrastructure on AWS. Partner with engineering and product teams to deliver production-ready ETL/ELT systems, data platforms, and AI workflows.

## Job Description

## 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

## Requirements
- 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

## Nice-to-Haves
- Experience with AWS Glue, EMR, or PySpark
- Experience with Snowflake data warehouse optimization and cost management

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