Senior Data Science Engineer

Adobe Adobe · Enterprise · San Jose, CA

Senior Data Science Engineer role focused on building and owning scalable data pipelines and ETL/ELT workflows for Adobe's Firefly generative AI products. The role involves supporting various teams including Finance, Credit Metering, AI-powered analytics, and model engineering, with a focus on data foundation, operational stability, and critical initiatives. Responsibilities include architecting data pipelines, building infrastructure for NLP analysis, creating reporting pipelines for generative credit usage and RLHF research data, and developing foundational datasets for AI-powered analytics.

What you'd actually do

  1. 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
  2. Build and maintain infrastructure for timely clustering, translation pipelines, and NLP analysis supporting modeling, data science, and analytics teams
  3. Build robust reporting pipelines for product and business-critical systems including generative credit usage data, feedback systems, RLHF research data, and partner reporting
  4. Build foundational datasets and systems that support advanced self-serve analytics powered by AI
  5. Define and promote coding standards, architecture patterns, and engineering guidelines across the team

Skills

Required

  • SQL
  • Python
  • Databricks
  • Spark SQL
  • dbt
  • Airflow
  • AWS
  • Azure
  • data engineering
  • data quality
  • data architectures

Nice to have

  • NLP pipelines
  • timely data processing
  • ML feature engineering
  • AI-powered analytics tools
  • self-serve data products
  • A/B testing infrastructure
  • experimentation data pipelines
  • data governance frameworks
  • data security
  • compliance requirements
  • Tableau
  • PowerBI

What the JD emphasized

  • track record of owning complex, production-grade data systems
  • 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

Other signals

  • building foundational datasets and systems that support advanced self-serve analytics powered by AI
  • build robust reporting pipelines for product and business-critical systems including generative credit usage data, feedback systems, RLHF research data, and partner reporting
  • partner with data analysts, data scientists, ML engineers, and product teams to anticipate evolving data needs and influence the technical roadmap