Data Engineer

Project Role : Data Engineer Project Role Description : Design, develop and maintain data solutions for data generation, collection, and processing. Create data pipelines, ensure data quality, and implement ETL (extract, transform and load) processes to migrate and deploy data across systems. Must have skills : Data Engineering Good to have skills : NA Minimum 7.5 year(s) of experience is required Educational Qualification : 15 years full time education Summary- Description: As a Data /ML Engineer, you will define the data requirements and structure for the application. Embedded FDE engineer (5–10 yrs) building AI-first SAP cloud applications and end-to-end solutions with customers. You will collaborate with various teams to ensure that the data Engineer is robust, scalable, and efficient, while also addressing any challenges that arise during the development process. Your role will be pivotal in shaping the data landscape of the organization, enabling data-driven decision-making and fostering innovation through effective data management practices. Responsibilities: Discover customer problems and frame AI-driven solutions. Design and implement end-to-end cloud architectures and production apps on SAP BTP. Build AI/LLM/Agentic features (RAG, hybrid retrieval) and integrate ML services. Ensure quality, security, observability, and CI/CD. Collaborate closely with customer teams, product, UX, data, and architecture. Prepare Gen AI-compatible datasets: manage vector embeddings, index with Databricks Vector Search, and use Feature Store with ML flow. Package and deploy pipelines using Databricks Asset Bundles through CI/CD pipelines in GitHub or GitLab. Troubleshoot, tune, and optimize jobs using Photon engine and serverless compute, ensuring cost efficiency and SLA reliability. Experience with cloud-based services relevant to data engineering, data storage, data processing, data warehousing, real-time streaming, and serverless computing. Hands on Experience in applying Performance optimization techniques Understanding data modeling and data warehousing principles is essential. 5–10 yrs software engineering for cloud/enterprise/SaaS. Proven delivery of multi-tenant, cloud-native apps. Hands-on with LLMs, RAG, agentic frameworks (e.g., LangChain, LangGraph, MCP). Strong Python/ML stack, REST/gRPC integration, vector DBs. SAP BTP & HANA experience Databricks or similar data pipelines. Nice to Have: 1. Certifications: Data Engineering Certified Professional or similar certifications. 2. Machine Learning: Knowledge of machine learning concepts and experience with popular ML libraries. 3. Knowledge of big data processing (e.g., Spark, Hadoop, Hive, Kafka) 4. Data Orchestration: Apache Airflow. 5. Knowledge of CI/CD pipelines and DevOps practices in a cloud environment. 6. Experience with ETL tools like Informatica, Talend, Mati Llion, or Five Tran. 7. Familiarity with DBT (Data Build Tool) Additional Information: