Data Engineer

We are seeking a Data Engineer to build and maintain Azure-based data pipelines and systems that drive analytics, machine learning, and product features. You will work with large-scale datasets, optimize performance, and ensure data is structured, accessible, and reliable.

This role is ideal for an engineer who enjoys hands-on technical work, solving real-world data challenges, and scaling infrastructure using Azure-native services.

Key Responsibilities

• Design, develop, and optimize ETL/ELT pipelines using Azure Data Factory, Synapse Pipelines, or Databricks.
• Maintain and evolve data lakes (ADLS Gen2), data warehouses (Synapse Analytics), and relational databases (Azure SQL, Cosmos DB).
• Implement data ingestion from APIs, event streams, files, and operational systems.
• Develop data models optimized for analytics and ML workflows.
• Collaborate with analytics, science, and governance teams to deliver trusted, documented datasets.
• Ensure efficient pipeline orchestration and scheduling using Azure Data Factory or similar tools.
• Optimize cloud usage for cost-efficiency and performance.

Requirements

Must-Have:

• 1–5 years of experience in Data Engineering, Software Engineering, or related fields.
• Strong skills in SQL and Python for data processing and scripting.
• Hands-on experience with Azure Data Services:
• Azure Data Factory (ADF)
• Azure Synapse Analytics
• Azure Data Lake Storage Gen2 (ADLS)
• Azure SQL Database / Cosmos DB
• Understanding of data modeling (star/snowflake schemas), partitioning, and performance tuning.
• Familiarity with CI/CD pipelines and version control systems (e.g., Git, Azure DevOps).

Nice-to-Have:

• Experience with Azure Databricks and Delta Lake for large-scale processing.
• Exposure to event-driven architectures (Azure Event Hubs, Kafka) and real-time streaming.
• Knowledge of dbt for transformation and modular ELT pipelines.
• Familiarity with Google Big Query