Azure Data Engineer

On-site
Posted Oct 23, 2026
1 position

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Employment type: Full-time
Experience level: Entry, Junior · 0+ years
Posting language: English
Working hours: 40 hours per week
Seniority: Entry level
Application method: Direct apply is available

Job summary

Design, build, and optimize scalable batch and streaming data pipelines and lakehouse or data warehouse solutions using Azure Data Factory, Databricks, Spark, and Azure Data Lake Storage Gen2. Ensure data quality, security, monitoring, cost efficiency, governance, and documentation while collaborating with analytics, data science, BI, and engineering teams.

Job details

Key Responsibilities Design, develop, and maintain scalable ETL/ELT pipelines using Azure Data Factory (ADF). Build data transformation workflows using Azure Databricks, Apache Spark, and PySpark. Extract, transform, and load data from multiple sources, including databases, APIs, files, and cloud storage. Develop and optimize SQL queries, stored procedures, and data transformation logic. Work with Azure Data Lake Storage Gen2 to store and manage structured and unstructured data. Design and implement data lakehouse and data warehouse solutions. Build batch and, where required, streaming data processing pipelines. Implement data validation, error handling, logging, and monitoring. Optimize pipeline performance, data processing efficiency, and cloud resource costs. Implement security measures using Microsoft Entra ID, Azure Key Vault, managed identities, and role-based access control. Collaborate with Data Analysts, Data Scientists, BI Developers, and other engineering teams. Maintain technical documentation and follow data governance and compliance standards. Required Technical Skills Strong knowledge of SQL, including joins, CTEs, window functions, and query optimization. Programming experience with Python and PySpark. Hands-on experience with Azure Data Factory. Experience with Azure Databricks and Apache Spark. Knowledge of Azure Data Lake Storage Gen2. Understanding of ETL/ELT processes and data pipeline architecture. Knowledge of data modeling, dimensional modeling, and data warehousing. Experience working with file formats such as CSV, JSON, Parquet, and Delta. Familiarity with Git, version control, and deployment practices.

What you’ll do

Requirements

Requires strong SQL skills, Python and PySpark experience, and hands-on experience with Azure Data Factory, Azure Databricks, Apache Spark, and Azure Data Lake Storage Gen2. Candidates should understand ETL/ELT, pipeline architecture, data modeling, dimensional modeling, data warehousing, common data formats, Git, and deployment practices.