Strong Azure Cloud-based data engineering background with hands-on expertise across the full data pipeline lifecycle — ingestion, transformation, orchestration, and analytics — including on-prem to cloud migration experience and Agile/DevOps delivery exposure.

Design and build scalable data pipelines and ETL workflows on Azure Develop data ingestion, transformation, and orchestration solutions using ADF, Databricks, and Synapse Collaborate with solution architects and business stakeholders to translate data requirements into technical solutions Implement data lakehouse and warehouse architectures for analytics and reporting Optimize existing pipelines for performance, cost efficiency, and maintainability Manage data integration, data quality, and error-handling mechanisms Support cloud migration projects and ensure alignment with architectural standards Apply security best practices, including RBAC, Key Vault, and managed identities Participate in CI/CD pipeline setup, DevOps integration, and automated deployments Troubleshoot production issues and perform root cause analysis for data failures Document technical design, workflows, and best practices for knowledge sharing Collaborate with cross-functional teams to deliver high-quality, reliable, and compliant data solutions

Azure Data Factory (ADF) — pipelines, triggers, linked services, data flows Azure Databricks — PySpark, Delta Lake, notebooks Azure Synapse Analytics — data warehouse design and optimization Azure Data Lake Storage (ADLS) — hierarchy, partitions, access control Azure SQL Database / Managed Instance Python, PySpark, and SQL for data transformations Batch and streaming data ingestion from multiple sources DevOps, CI/CD, Infrastructure-as-Code (Terraform / ARM templates) Azure Security, IAM, and networking fundamentals Data lakehouse design, ETL/ELT pipelines, and data modeling Cost optimization, monitoring, and logging (Azure Monitor, Log Analytics) Containerization (Docker, Kubernetes, AKS) — preferred Secondary Skills: Power BI or other visualization tools Informatica, ADF Mapping Data Flows, or similar ETL tools Data quality, cataloging, and governance frameworks (Purview) Agile methodologies and sprint-based delivery Basic ML workflow awareness (Databricks ML, Azure ML)