Senior Data Engineer

The Opportunity 

Firefly is Adobe's family of creative generative AI products — and it's growing fast. Our data team sits at the center of that growth, powering the analytics, modeling, and product decisions that build what Firefly becomes. We're hiring a Senior Data Engineer to improve our data foundation. This person not only builds pipelines but also plans them carefully. They take action on needs they see without waiting for instructions. 

This role will have immediate and visible impact in multiple high-stakes areas. You will support Finance, Credit Metering, AI-powered self-serve analytics, and Firefly's model engineering and NLP teams. As a core data engineering resource on a growing team, you will help maintain operational stability and speed up critical initiatives.  

What You'll Do 

- Architect, build, and own scalable data pipelines and ETL/ELT workflows across multiple sources into a central data warehouse - with end-to-end accountability for data mapping, business logic, quality, and lineage 

• Build and maintain infrastructure for timely clustering, translation pipelines, and NLP analysis supporting modeling, data science, and analytics teams 

• Build robust reporting pipelines for product and business-critical systems including generative credit usage data, feedback systems, RLHF research data, and partner reporting 

• Build foundational datasets and systems that support advanced self-serve analytics powered by AI 

• Define and promote coding standards, architecture patterns, and engineering guidelines across the team 

• Build data infrastructure that supports AI-powered insights and reduces dependency on ad hoc requests 

• Partner with data analysts, data scientists, ML engineers, and product teams to anticipate evolving data needs and influence the technical roadmap 

What You'll Need to Succeed 

• BS or MS or PhD in an analytical field: statistics, applied mathematics, computer science, engineering, economics, physics, or equivalent practical experience 

• 5+ years of hands-on data engineering experience with a track record of owning complex, production-grade data systems 

• Strong proficiency in SQL and Python; deep hands-on experience with Databricks, Spark SQL, dbt, Airflow, and cloud platforms (AWS and/or Azure) 

• Demonstrated experience crafting scalable data architectures — not just implementing them, but making the trade-off decisions and owning the outcomes 

• Strong instincts for data quality: building reliability and observability by default, not as an afterthought 

• Ability to take ambiguous business needs and translate them into well-scoped, independently implemented data solutions 

• Strong communication skills with the ability to explain technical architecture decisions to non-technical partners. 

Nice to Have 

• Experience with NLP pipelines, timely data processing, or ML feature engineering 

• Experience buildi