Data Science Engineer

Apply your expertise in data engineering to drive the next stage of growth at Adobe. The AUP team is passionate about delivering optimized experiences through personalization. This role will drive data engineering for large‑scale data science initiatives across a wide variety of strategic projects. Responsibilities Build fault tolerant, scalable, quality data pipelines using multiple cloud‑based tools. Build analytical, personalization capabilities using modern and brand new technologies employing Adobe tools like AEP, AJO and CJA. Build LLM agents to optimize and automate data pipelines following best engineering practices. Deliver end‑to‑end data pipelines to run machine learning models in a production platform. Innovative solutions to help the broader organization take significant actions fast and efficiently. Chip in to data engineering and data science frameworks, tools, and processes. Implement outstanding data operations and implement standard methodologies to use resources in an optimum way. Architect data ingestion, data transformation, data consumption, data governance frameworks. Help build production‑grade ML models and integration with operational systems. Work in a collaborative environment and contribute to the team as well as organization's success. Qualifications Master's degree or equivalent experience is preferred. 8+ years of consistent track record as a data engineer. 5+ years validated ability in distributed data technologies e.g., Hadoop, Hive, Presto, Spark etc. 3+ years of experience with Cloud based technologies - Databricks, S3, Azure Blob Storage, Notebooks, AWS EMR, Athena, Glue etc. Familiarity and usage of different file formats in batch/streaming processing i.e., Delta/Parquet/ORC etc. 2+ years of experience with streaming data ingestion and transformation using Kafka, Kinesis etc. Outstanding SQL experience. Ability to write optimized SQLs across platforms. Proven hands‑on experience in Python/PySpark/Scala and ability to manipulate data using Pandas, NumPy, Koalas etc. and using APIs to transfer data. Experience with CI/CD tools i.e., GitHub, Jenkins etc. Working experience with open‑source orchestration tools i.e., Apache Air Flow/ Azkaban etc. Teammate with excellent communication/teamwork skills when it comes to closely working with data scientists and machine learning engineers daily. Nice to Have Showcase your work if you are an open-source contributor. Passion to contribute to Open-source community is highly valued. Experience with Data Governance tools e.g., Collibra and Collaboration tools e.g., JIRA/ Confluence etc. Familiarity with Adobe solutions such as Adobe Experience Platform, Adobe Analytics, Customer Journey Analytics, and Adobe Journey Optimizer is a plus. Experience with LLM Models/ Agentic workflows using Copilot, Claude, LLAMA, Databricks Genie etc. is highly preferred. Skills in building context and prompt engineering solutions including classical RAG, Knowledge graph, MCPs, Agentic frameworks like n8n, etc. are highly desirable. Expected Pay Range U.S. pay range for this position is $109,000 – $200,700 annually. In California, the pay range is $138,600 – $200,700. Equal Employment Opportunity Adobe is an Equal Employment Opportunity employer. We do not discriminate based on gender, race or color, ethnicity or national origin, age, disability, religion, sexual orientation, gender identity or expression, veteran status, or any other protected characteristic. State Specific Notices California: Adobe will consider qualified applicants with arrest or conviction records for employment in accordance with state and local laws and "fair chance" ordinances. Colorado: If this role is open to hiring in Colorado, the application window will remain open until at least the date and time stated above in Pacific Time, in compliance with Colorado pay transparency regulations. Massachusetts: It is unlawful to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability. #J-18808-Ljbffr