Data Engineer (Azure/Assistant Manager)

Roles & responsibilities

•Participate in the design, development, and delivery of Azure data engineering solutions for large-scale data platforms

•Work directly with clients to gather requirements for data ingestion, integration, transformation, storage, and processing

•Develop and maintain ETL/ELT pipelines using Azure Data Factory, Synapse, Databricks, and other Azure-native services

•Implement highly scalable data lake and data warehouse architectures aligned with best practices

•Contribute to data modernization, cloud migration, and integration programs across industries

•Ensure data platform performance, reliability, security, and governance in alignment with organizational and compliance standards

•Collaborate closely with cloud architects, BI teams, and data scientists to support end-to-end data lifecycle needs

•Continuously learn new Azure data services and engineering frameworks, adopting industry best practices

•Optimize Azure resource consumption through cost management and performance tuning

•Support documentation, design reviews, sprint planning, and technical delivery discussions

Educational qualifications

•A bachelor's or master's degree in Engineering, Computer Science, or equivalent

•Fabric Data Engineer Certificate (DP-700)

Work experience

•Hands-on experience (6–8 years) in Azure data engineering, data integration, and data platform development

•Experience working on ETL/ELT pipelines, data lakes, relational and NoSQL databases, and real‑time/batch ingestion

•Exposure to data migration, modernization, governance, and cloud-native data architectures

Mandatory technical & functional skills

•Strong hands-on expertise in Azure Data Factory, Azure Synapse Analytics, Azure Data Lake Storage Gen2, and Azure SQL / T-SQL, Azure Fabric

•Experience with Azure Databricks, PySpark, and Python for large-scale data processing

•Good understanding of data modeling, star/snowflake schema, and modern analytical design patterns

•Strong proficiency in SQL, query tuning, and working with structured and semi-structured data

•Experience with stream processing using Event Hubs, Kafka, or similar technologies

•Familiarity with REST APIs and integration patterns

•Understanding of data security, RBAC, IAM, encryption, and compliance in cloud environments

•Experience working with DevOps and CI/CD pipelines for data engineering workloads

•Practical understanding of Agile methodologies and data engineering best practices

•Strong communication skills with the ability to collaborate across teams and contribute to client discussions

Preferred technical & functional skills

•Experience with serverless components such as Azure Functions or Logic Apps

•Knowledge of Microsoft Purview for data governance, lineage, and cataloging

•Familiarity with implementing data quality frameworks

•Exposure to advanced analytics, machine learning enablement, or Power BI dataflows

•Understanding of Generative AI concepts, data preparation for LLMs, and integration with Azure OpenAI services