Senior Data Engineer

Adobe•San Jose, CA

2d•$133,100 - $236,400

About The Position

Firefly is Adobe's family of creative generative AI products, and the data team is central to its growth, powering analytics, modeling, and product decisions. This role focuses on improving the data foundation by architecting and building scalable data pipelines and ETL/ELT workflows. The Senior Data Engineer will support various critical areas including Finance, Credit Metering, AI-powered self-serve analytics, and model engineering/NLP teams. This position offers immediate and visible impact, contributing to operational stability and accelerating key initiatives within a growing team.

Requirements

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 Haves

Experience with NLP pipelines, timely data processing, or ML feature engineering
Experience building or supporting AI-powered analytics tools or self-serve data products
Background in A/B testing infrastructure and experimentation data pipelines
Exposure to data governance frameworks, data security, and compliance requirements
Experience crafting dashboards in Tableau or PowerBI etc

Responsibilities

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