Data Engineer

We’re looking for a talented Data Engineer (Junior–Mid Level) to join our technology team and help shape and optimize our Microsoft Fabric data platform. The ideal candidate has hands-on experience in cloud data engineering with Azure, Synapse, or Databricks, is skilled in PySpark, SparkSQL, and data lakehouse architectures, and brings strong problem-solving abilities, a proactive mindset, and excellent teamwork skills. A Bachelor’s degree in Computer Science, Information Systems, Data Engineering, or a related field is required, and fluency in English, both written and verbal, is a must.

Key Responsibilities:

• Build and maintain large-scale data pipelines and transformations using SQL, PySpark, and SparkSQL.
• Ingest and process data from diverse cloud sources and design/manage data layers (Bronze → Silver → Gold) in Fabric Lakehouse.
• Leverage Fabric Pipelines and Azure DevOps for orchestration, automation, and CI/CD deployments.
• Collaborate closely with analysts and business users to deliver reliable, well-modeled data for reporting and dashboards.

Requirements:

1-3 years of experience working with large datasets in cloud environments (preferably Microsoft Fabric or Azure Synapse \ Data Factory \ Databricks).

•             Understanding of data modeling, ELT \ ETL design patterns, and orchestration concepts.

•             Strong SQL skills for data transformation and validation.

•             Hands-on experience writing efficient PySpark\ SparkSQL for data transformation and aggregation.

•             Experience working with Lakehouse architecture - managing data layers (Bronze → Silver → Gold).

•             Knowledge of Delta tables, schema evolution, partitioning, and Parquet optimization and performance tuning for big data.

•             Experience building and monitoring pipelines - ideally Fabric Data Pipelines, Azure Data Factory, or Synapse Pipelines.

•             Familiarity with logging, error handling, and alerting in data workflows.

•             Understanding of relational modeling, star schema, and joins across Fact\ Dimension tables.

•             Basic understanding of CI\CD practices.