Data Engineer

Role & responsibilities

Required Technical Skills

• Primary Languages (Must Have): Python, SQL and React.
• Secondary Languages (Working knowledge): Node.js/JavaScript, Java and Scala
• Backend: FastAPI, REST APIs, microservices, and Spring Boot or equivalent.
• Data Engineering: Apache Spark/PySpark, Databricks, Delta Lake/Delta Tables, ETL/ELT, and data pipelines.
• Cloud: Azure preferred; AWS/GCP experience is also valuable.
• Databases: SQL and NoSQL databases; data warehouse/lakehouse experience is desirable.
• GenAI: LLMs, RAG, embeddings, vector databases, prompt engineering, AI agents, and LLM APIs.
• DevOps: Git, Docker, CI/CD, Kubernetes or equivalent deployment technologies.
• Data orchestration: Airflow, Azure Data Factory, Databricks Workflows, or similar.

Good to Have

• Azure Databricks and Microsoft Fabric experience.
• Azure OpenAI or other foundation-model API experience.
• Experience building production-grade GenAI applications.
• Knowledge of ML/AI concepts and model integration.
• Terraform or Infrastructure as Code experience.
• Kafka or other streaming technologies.
• Strong understanding of data architecture and lakehouse architecture.

Experience

5+ years of relevant software/data engineering experience, with strong hands-on development experience.