Architect and lead the evolution of our modern data platform, driving technical decisions on tooling, infrastructure patterns, and scalability strategies that support both traditional analytics and AI/ML workloads at scale
Design and build production LLM pipelines and infrastructure that power intelligent operations.
Own end-to-end data acquisition and integration architecture across diverse sources (CRMs, clickstream, third-party APIs), establishing patterns and frameworks that enable self-service data access while maintaining data quality and governance
Create shared abstractions and tooling for AI – for example, common prompt and tool patterns, logging and monitoring, and reusable components – so other engineers can build on a consistent foundation.
Shape our data and system architecture so AI can safely stitch together longitudinal signals across product, billing, support, and operations and recommend what should happen next, not just report what happened.
Lead by example in AI-augmented engineering, using AI to multiply your own speed, mentoring L2/L3 engineers, and raising the bar for how we design, ship, and operate AI-powered features.
Mentor and influence engineering culture, conducting design reviews, providing technical guidance to engineers across the organization, and championing data platform adoption and best practices

Requirements

6+ years of work experience as a data engineer, backend engineer, full stack or DevOps engineer with strong proficiency in Python and modern data engineering practices
Applied AI impact at scale: Proven track record of shipping AI / LLM-powered features into production with clear, measurable impact on key metrics (for example, engagement, time saved, satisfaction, or retention), ideally across more than one product area.
Hands-on experience with large language models (LLMs) in real applications, including prompt and tool design, retrieval-style patterns (such as RAG), and evaluation and monitoring in production.
Strong computer science fundamentals (e.g., data structures, algorithms, and systems design) and a generalist mindset, comfortable moving between backend, data, and UX to get the job done.
Experience designing, developing, and deploying ML/LLM/AI pipelines in production environments, including experience with model serving, feature engineering, and MLOps practices
Expert-level understanding of distributed data processing technologies and their internals (e.g., Spark execution model, query optimization in Redshift/BigQuery/Snowflake, storage formats like Parquet/ORC)
Proven track record of independently architecting scalable data solutions, from requirements gathering and technical design through implementation and cost optimization, with focus on long-term maintainability and ROI.

Benefits

Comprehensive medical, dental, and vision coverage
Generous paid parental leave
Flexible PTO so you can recharge when you need it
Local retirement or savings plans (e.g., 401(k) in the U.S.)

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

Pythondata engineering practicesAI/ML pipelineslarge language models (LLMs)prompt designretrieval-style patternsMLOpsdistributed data processingSparkdata structures

Soft Skills

mentoringtechnical guidanceinfluencing engineering culturedesign reviewsleadershipcommunicationcollaborationproblem-solvingadaptabilitycritical thinking