Data Engineer

1. Full-Stack + Data Engineer

Role Overview

We are looking for a Full-Stack + Data Engineer with strong hands-on experience across backend development, data engineering, cloud technologies, and modern GenAI solutions. The ideal candidate should be comfortable building scalable applications as well as designing and implementing data pipelines and analytics platforms.

Key Responsibilities

• Design, develop, and maintain scalable full-stack applications and backend services.
• Build robust data pipelines, ETL/ELT workflows, and data processing solutions.
• Develop APIs and microservices using Java, Python, and Node.js.
• Build production-grade APIs using FastAPI and other modern Python frameworks.
• Design and optimize data solutions using Databricks, Delta Lake/Delta Tables, Apache Spark/PySpark, and SQL.
• Build data ingestion, transformation, validation, and orchestration pipelines.
• Work with cloud platforms, preferably Microsoft Azure, across compute, storage, databases, data engineering, and AI/ML services.
• Develop and integrate GenAI applications, LLM-based solutions, RAG pipelines, AI agents, and intelligent automation.
• Work with vector databases, embeddings, prompt engineering, model APIs, and LLM frameworks.
• Collaborate with frontend, backend, data, AI/ML, and product teams to deliver end-to-end solutions.
• Apply best practices for security, scalability, performance, testing, CI/CD, and observability.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.