Information Systems Engineer 4

Summary We are seeking a Data Engineer / Data Migration / Integration Engineer to build, develop, and support scalable data platforms, migration solutions, and integration frameworks. These systems enable business-critical analytics, operational processes, and capabilities powered by artificial intelligence. This role combines data engineering, integration engineering, cloud technologies, and data architecture prepared for AI.

Build, develop, test, and deploy scalable data pipelines using modern cloud technologies. Build batch, micro-batch, and real-time data processing solutions. Develop ETL/ELT frameworks and reusable integration components. Complete data migration and modernization initiatives across cloud and hybrid environments. Build and maintain REST API, event-driven, and Kafka-based integrations. Build cloud-native data solutions using AWS and Azure services. Implement data quality, observability, monitoring, and governance practices. Help shape the future of AI-enabled products by creating secure, reliable, and scalable data foundations that power Generative AI and intelligent experiences. Technical Skills & Qualifications Required Qualifications Bachelor's degree in Computer Science, Engineering, Information Systems, or related field. 6–12 years of experience in Data Engineering, Data Migration, Integration Engineering, or related roles. Strong programming skills in Python and SQL with working knowledge of Java or Scala. Experience with Spark, PySpark, Databricks, and distributed data processing frameworks. Hands-on experience with AWS and/or Azure data platforms. Experience building ETL/ELT pipelines, integrations, and migration solutions. Familiarity with REST APIs, event-driven architectures, streaming technologies, CI/CD, and automation.

Experience with Snowflake, Delta Lake, Airflow, Kafka, SnapLogic, ADLS, and SQL Server. Exposure to Salesforce (SFDC) or Microsoft Dynamics 365 (D365). Experience with workflow orchestration tools such as Airflow, Tidal, or equivalent platforms. Understanding of AI, Generative AI, and emerging AI-driven engineering practices that improve developer productivity and customer experiences. Core Technology Stack Python, SQL, PySpark, Spark SQL, Databricks, Snowflake, Airflow or Tidal, Kafka, REST APIs, AWS, Azure, GitHub , Delta Lake, SnapLogic, ADLS, SQL Server, AI-assisted Development AI & Future-Ready Engineering Expectations Leverage AI-assisted development tools to accelerate coding, testing, debugging, and documentation. Validate AI-generated outputs for correctness, security, performance, and maintainability. Understand foundational concepts of LLMs, Retrieval-Augmented Generation (RAG), embeddings, vector search, and AI data pipelines. Contribute to AI-first engineering practices and responsible AI adoption. Success Indicators Deliver high-quality, production-ready solutions with minimal rework. Own medium-complexity data engineering, migration, and integration initiatives from development through deployment. Build scalable and maintainable solutions that improve business outcomes. Improve automation, reliability, performance, and operational efficiency. Contribute to AI-ready data foundations supporting future analytics and intelligent applications. Why Join Us You will work on large-scale data platforms, cloud modernization initiatives, and enterprise integrations. You will also contribute to next-generation AI-enabled solutions while collaborating with high-performing teams and crafting the future of data-driven innovation! About Adobe Adobe empowers everyone to create through innovative platforms and tools that unleash creativity, productivity and personalized customer experiences. Adobe’s industry-leading offerings including Adobe Acrobat Studio, Adobe Express, Adobe Firefly, Creative Cloud, Adobe Experience Platform, Adobe Experience Manager, and GenStudio enable people and businesses to turn ideas into impact,