Azure Data Engineer

Requirements

• 5+ years of experience in data engineering, data architecture, or related roles
• Proven track record of designing and deploying data pipelines using Azure services
• Hands-on experience with cloud data solutions in a commercial or enterprise environment
• Experience working with cross-functional teams on large-scale data projects
• Familiarity with big data processing frameworks and emerging data technologies
• Day-to-Day Activities
• Develop, test, and deploy scalable data pipelines for ingestion, transformation, and storage
• Collaborate with data analysts, scientists, and stakeholders to understand data requirements
• Optimize ETL processes for performance, scalability, and cost-efficiency
• Monitor data pipeline health and troubleshoot issues proactively
• Implement data security, privacy, and governance measures
• Automate deployment workflows and manage infrastructure as code on Azure
• Document data architecture, workflows, and standards
• Stay current with new Azure data services and best practices to recommend improvements
• Qualifications & Soft Skills
• Bachelor's degree in Computer Science, Engineering, Data Science, or related field
• 5+ years of experience in data engineering, data architecture, or related disciplines
• Relevant Azure certifications (e.g., Microsoft Certified: Azure Data Engineer Associate) are preferred
• Soft Skills:
• Strong problem-solving, analytical and organizational skills
• Excellent communication skills to interface with technical and non-technical stakeholders
• Ability to work independently and collaboratively in a team environment
• Attention to detail and commitment to data quality
• Adaptability to evolving technologies and project priorities
• Proactive mindset with continuous learning orientation
• S YNECHRON'S DIVERSITY & INCLUSION STATEMENT
• All employment decisions at Synechron are based on business needs, job requirements and individual qualifications, without regard to the applicant's gender, gender identity, sex

Benefits

Additional Information

Overall Responsibilities Design and develop scalable, reliable data pipelines on Azure to collect, process, and store large datasets Implement efficient data models and data warehousing solutions aligning with business requirements Collaborate with data scientists, analysts, and application teams to integrate data workflows Optimize data pipeline performance, reliability, and security Support data governance, compliance, and data privacy standards Monitor operational health of data solutions and troubleshoot issues proactively Document architecture, workflows, and best practices for data ingestion and transformation Stay updated with the latest Azure data platform innovations and incorporate improvements Software Requirements Proficiency in SQL and Python for data processing and scripting Hands-on experience with Azure Data Services such as Azure Data Factory, Azure Data Lake, Azure Databricks, and Azure Synapse Analytics Understanding of data warehousing, data modeling, and ETL/ELT processes Experience implementing data pipelines for ingestion, transformation, and storage Knowledge of Azure Blob Storage, Cosmos DB, and relational databases (Azure SQL Database, SQL Server) Familiarity with DevOps practices for deployment and continuous integration in the cloud Experience with Spark, Scala, or Java in Azure Databricks Knowledge of Power BI or other data visualization tools Exposure to DataOps, automation, and monitoring tools within Azure Category-wise Technical Skills Data Processing & Frameworks: Azure Data Factory, Azure Databricks, Azure Data Lake, Synapse Analytics Python, PySpark, Scala, or Java for data processing SQL, T-SQL, and experience with relational and non-relational data stores Cloud & Infrastructure: Azure AI & Machine Learning services (optional) Azure Storage options: Blob, Data Lake, Cosmos DB Azure DevOps, CI/CD pipelines, Infrastructure as Code (ARM templates, Terraform) Data Modeling & Warehousing: Star schema, data marts, normalization/denormalization principles Data governance, lineage, and security standards Tools & Monitoring: Power BI, Azure Monitor, Log Analytics for data visualization and monitoring Version control systems like Git

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