Data Engineer

Job Description:

Senior Data Engineer – Content Ops & Site Quality

Role Overview

We are looking for a Senior Data Engineer to support the Data Engineering ecosystem for Content Operations and Site Quality . The role will involve managing and enhancing large-scale data pipelines, data models, analytics infrastructure, and AI-driven solutions that support business and analytics teams.

The ideal candidate will have strong hands-on experience in SQL, PySpark, data engineering, cloud/data platforms, and a working understanding of Adobe Analytics and AI/LLM-based solutions.

Key Responsibilities

Data Engineering & Platform Management (Must have)

• Own and manage Data Engineering activities supporting Content Ops and Site Quality.
• Develop, maintain, and optimize data pipelines supporting approximately 150 MDP tables, 2 SSAS Cubes, and multiple Airflow workflows.
• Build and maintain scalable data processing solutions using SQL Server, Iceberg, PySpark, ECS, and Airflow.
• Troubleshoot data quality, pipeline, performance, and production issues and drive them through to resolution.
• Support data modeling and development of reliable datasets for downstream analytics and reporting.

AI & Analytics Solutions (Preferred)

• Develop and enhance AI/LLM-based solutions, including RAG and agent-based applications.
• Contribute to an AI agent/RAG solution that enables users to translate Adobe Analytics requirements into SQL queries.
• Maintain and enhance the Client’s chatbot assistant, including updates, new capabilities, and ongoing performance improvements.
• Identify opportunities to leverage AI and automation to simplify data and analytics workflows.

Stakeholder & Solution Development

• Partner with business and analytics stakeholders to understand requirements and translate them into scalable technical solutions.
• Contribute to solution design, technical discussions, and roadmap development.
• Clearly communicate complex technical concepts and solutions to non-technical stakeholders.

Required Skills & Experience

• Strong hands-on experience in Data Engineering with SQL and PySpark.
• Experience with SQL Server, SSAS, Airflow, and modern data platforms.
• Experience working with Iceberg and cloud/container-based environments such as ECS.
• Strong understanding of data modeling, ETL/ELT, data pipelines, and data quality.
• Experience with Adobe Analytics or similar digital analytics platforms.
• Exposure to AI/LLM solutions, RAG, AI agents, or Generative AI applications.
• Strong problem-solving and troubleshooting skills.
• Ability to work independently and manage multiple production-critical data assets.

Preferred Skills

• Experience building RAG/agentic AI applications using LLMs.
• Experience converting business/analytics requirements into SQL and data solutions.
• Experience with APIs, Python, and cloud technologies.
• Experience developing stakeholder-facing analytics or AI solutions.
• Strong communication and stakeholder management skills.

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