Middle Data Engineer

Responsibilities

• Design and develop scalable and reliable data platforms and/or data pipelines for analytical and operational use cases
• Design and implement data processing workflows using distributed processing frameworks such as Apache Spark/pySpark (Databricks)
• Contribute to the evolution of the organization’s cloud data platform using Azure technologies (e.g., Azure Data Factory, Synapse, Microsoft Fabrik, Databricks, Event Hub, Azure Data Lake Storage)
• Strong background in data modelling to support analytics, reporting, and downstream data consumption
• Integrate and process data from multiple internal and external sources while ensuring data consistency, quality, and reliability
• Take ownership of data pipeline reliability, monitoring, and performance, ensuring scalable and maintainable data processing workflows
• Collaborate with data analysts, data scientists, and engineering teams to translate business and analytical requirements into robust data solutions
• Drive improvements in data engineering practices, including pipeline design standards, testing approaches, and operational reliability
• Participate in evaluating and introducing new tools and technologies within the data engineering ecosystem

Required Qualifications

• Strong hands-on experience (4+ years) in data engineering or building data processing platforms
• Strong SQL skills and solid experience with Python for building and maintaining data pipelines
• Experience working with distributed data processing frameworks such as Apache Spark/pySpark (Databricks experience is a strong advantage)
• Practical experience designing and implementing data pipelines in cloud environments, preferably within the Azure ecosystem
• Experience designing data pipelines and data models for production-scale analytical systems
• Strong understanding of data engineering concepts including ETL/ELT design, dimensional modeling, and data warehousing principles
• Experience working with modern data lake architectures and data formats such as Parquet and JSON
• Experience with workflow orchestration tools (e.g., Airflow)
• Experience with database development and performance optimization
• Ability to design scalable and maintainable data solutions with minimal supervision
• Strong collaboration and communication skills
• Professional working proficiency in English

Nice to Have

• Experience with modern cloud data warehouses such as Snowflake, BigQuery, or Redshift
• Experience with streaming data technologies (e.g., Event Hub, Kafka, Spark Streaming)
• Experience with Delta Lake or other lakehouse technologies
• Familiarity with CI/CD practices and Git-based development workflows
• Experience working in agile development environments