Senior Data Engineer

Data Engineer Experience: 2–4 years Location: Bengaluru (Hybrid) About the Company Akaike Technologies is a fast-growing AI-first organization focused on building real-world, high-impact AI systems across industries. We work at the intersection of Generative AI, Multimodal AI, and Large-Scale ML Engineering, enabling enterprises to operationalize cutting-edge AI solutions at scale. We foster a culture of ownership, deep technical rigor, and continuous learning. Experience Pre-Requisite: 2–4 years of hands-on experience in Data Engineering, with strong exposure to Databricks (PySpark, Spark SQL) and Azure Data Services (ADF, ADLS, Azure SQL). Candidates must demonstrate real-world experience in designing, building, deploying, and optimizing scalable data pipelines end-to-end. Job Description: We are seeking a highly skilled Data Engineer to design, develop, and deploy scalable data solutions on Databricks and Azure Data Services. This role requires deep technical expertise in PySpark, SQL, Delta Lake, and Medallion Architecture, combined with strong problem-solving ability and collaboration skills. The ideal candidate will take end-to-end ownership of data engineering workflows—from data ingestion and transformation to data modeling and governance—while ensuring performance, reliability, and security. You will work in a fast-paced, cloud-native environment, partnering with cross-functional teams to deliver production-grade data pipelines that enable advanced analytics and business insights. Key Responsibilities 1. Design and Development Design, develop, and deploy scalable data pipelines using Databricks (PySpark, Spark SQL), Azure Data Factory, and other Azure data services. Implement ETL/ELT processes to ingest, transform, and load data from diverse sources into data lakes and data warehouses. Optimize and tune data pipelines for performance, scalability, and cost efficiency. 2. Data Processing Write and optimize complex SQL queries for data extraction, transformation, and analysis. Utilize PySpark for large-scale data processing and advanced analytics. Implement data partitioning, bucketing, and indexing strategies for efficient data retrieval. 3. Data Integration Integrate data from multiple sources, including structured, semi-structured, and unstructured formats. Work with APIs, streaming data, and batch processing to ensure seamless data integration. 4. Data Governance and Quality Apply data governance practices to maintain data quality, consistency, and security. Monitor and troubleshoot data pipelines to ensure accuracy, availability, and reliability. 5. Collaboration Partner with data scientists, analysts, and other stakeholders to understand requirements and deliver solutions. Work closely with DevOps teams to deploy and monitor data pipelines in production environments. 6. Documentation Document data pipelines, workflows, and processes for knowledge sharing and future reference. Maintain up-to-date…