Senior AI / Data Engineer

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