Data Science Engineer

Apply your expertise in data engineering to drive the next stage of growth  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.

As a member of the Data Engineering team, you will have significant responsibility to help build a large‑scale cloud‑based data and analytics platform with enterprise‑wide consumers. This role is inherently multi‑functional, and the ideal candidate will work across teams. The position requires the ability to own things, come up with innovative solutions, and try new tools and technologies.

• 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 machine learning models and integration with operational systems.
• Work in a collaborative environment and contribute to the team as well as the organization’s success.

• 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 such as Hadoop, Hive, Presto, Spark.
• 3+ years of experience with cloud based technologies – Databricks, S3, Azure Blob Storage, Notebooks, AWS EMR, Athena, Glue, and familiarity with different file formats in batch/streaming processing such as Delta, Parquet, ORC.
• 2+ years of experience with streaming data ingestion and transformation using Kafka, Kinesis.
• Outstanding SQL experience with the ability to write optimized SQL across platforms.
• Proven hands‑on experience in Python, PySpark, Scala and ability to manipulate data using Pandas, Num Py, Koalas and using APIs to transfer data.
• Experience with CI/CD tools such as Git Hub and Jenkins.
• Working experience with open source orchestration tools such as Apache Airflow or Azkaban.
• Teammate with excellent communication and teamwork skills when working closely with data scientists and machine learning engineers daily.

• Showcase your work if you are an open‑source contributor. Passion to contribute to the open‑source community is highly valued.
• Experience with data governance tools such as Collibra and collaboration tools such as JIRA, Confluence.
• 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 or agentic workflows using Copilot, Claude, LLAMA, Databricks Genie, and skill in building context and prompt engineering solutions including classical RAG, knowledge graph, MCPs, agentic frameworks like n8n.

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