Senior Data Engineer - AI & Data

Employment Type: Full-Time, Permanent

Required Experience: 6–10 Years

We are seeking an experienced Senior Data Engineer to design, develop, and manage modern data platforms, scalable data pipelines, and cloud-based analytics solutions. The ideal candidate will have strong hands-on expertise in Microsoft Azure Data Services, large-scale data processing, data warehousing, ETL/ELT frameworks, and cloud-native data architectures.

The role involves building secure, reliable, and high-performance data solutions that support enterprise analytics, business intelligence, reporting, and AI/ML initiatives. The successful candidate will collaborate with cross-functional teams to deliver robust data engineering solutions aligned with business and technical requirements.

Design, develop, and maintain scalable data platforms and data pipelines using Microsoft Azure services.

Build and optimize batch and real-time data ingestion frameworks for structured, semi-structured, and unstructured data sources.

Design and implement data lake, data warehouse, and lakehouse architectures to support analytics and reporting requirements.

Develop, manage, and optimize ETL/ELT pipelines using modern cloud-native data engineering practices.

Implement data transformation, cleansing, validation, and data quality frameworks to ensure data accuracy and consistency.

Collaborate with business stakeholders, data analysts, data scientists, and application teams to translate requirements into scalable data solutions.

Optimize data storage, processing, and query performance across enterprise data platforms.

Implement data security, governance, monitoring, metadata management, and compliance best practices across Azure environments.

Integrate enterprise data platforms with AI/ML solutions, business intelligence tools, and enterprise applications.

Develop and maintain data models, reusable components, and efficient data processing workflows.

Support data platform reliability, scalability, high availability, and operational excellence.

Implement version control, CI/CD pipelines, and DevOps practices for data engineering workloads.

Bachelor’s degree in Computer Science, Information Technology, Software Engineering, or a related field.

• 6–10 years of professional experience in Data Engineering, Data Warehousing, or Enterprise Data Platform development.
• Strong hands-on experience with Microsoft Azure Data Services and cloud-based data engineering solutions.
• Practical expertise in Azure Data Factory (ADF), Azure Synapse Analytics, Azure Data Lake Storage Gen2 (ADLS Gen2), and Azure SQL Database.
• Experience designing, developing, and maintaining large-scale ETL/ELT pipelines and data integration frameworks.
• Strong proficiency in SQL, query optimization, and database performance tuning.
• Hands-on experience with PySpark, Apache Spark, and distributed data processing frameworks.
• Strong programming skills in Python, with exposure to Scala or Java being an advantage.
• Solid understanding of dimensional modeling, data warehousing principles, and modern lakehouse architectures.
• Experience processing structured, semi-structured, and unstructured data.
• Knowledge of data governance, data quality, metadata management, and data security best practices.
• Experience with REST APIs, enterprise data integration patterns, and connectivity with business applications.
• Familiarity with Git, CI/CD pipelines, and DevOps practices.
• Experience with Microsoft Fabric, OneLake, Dataflows, and Fabric Data Engineering workloads.

Hands-on experience with Databricks, Delta Lake, and lakehouse implementations.

Knowledge of real-time streaming technologies such as Azure Event Hubs, Apache Kafka, or Azure Stream Analytics.

Experience supporting AI/ML and advanced analytics workloads through enterprise data platforms.

Familiarity with Power BI datasets, semantic models, and enterprise reporting architectures.

Experience with data governance tools such as Microsoft Purview.

Microsoft Azure Data Engineering certifications are highly desirable.

Experience working in Agile/Scrum environments.

Experience with Microsoft Fabric Data Engineering and analytics solutions.

Knowledge of containerization technologies such as Docker and Kubernetes.

Experience with Infrastructure as Code tools, including Terraform, ARM templates, or Bicep.

Familiarity with Snowflake, Google BigQuery, or other cloud data warehouse platforms.

Experience with enterprise-scale data migration, modernization, and cloud transformation projects.

Understanding of Generative AI, vector databases, and data platforms supporting AI workloads.

Employment Type: Full-Time, Permanent

Required Experience: 6–10 Years

Work on modern enterprise data platforms and cloud-based analytics solutions.

Gain exposure to advanced Azure data engineering and lakehouse technologies.

Contribute to data initiatives supporting business intelligence, analytics, and AI/ML applications.

Participate in enterprise data modernization and continuous improvement initiatives.