Data Engineer

Required Experience & Qualifications:

• 5 to 7 years of core hands-on engineering experience building, monitoring, and maintaining enterprise ETL/ELT data pipelines.
• Technical Ingestion Frameworks: Strong execution background with Azure Data Factory (ADF), DBT (Data Build Tool), Apache Spark, or Python-based data frameworks.
• Schema Management: Proven track record of managing ground-level data warehouse implementations, system schemas, and automated data validation layers.

Must-Have Skillset:

• ETL/ELT Pipeline Development: 3+ years of experience constructing data pipelines utilizing tools such as Azure Data Factory (ADF), DBT, Apache Spark, or Airflow.
• Strong Database Fundamentals: Expert-level SQL skills and mid-to-advanced proficiency in Python or Scala for data manipulation and scripting.
• Cloud Platform Execution: Hands-on engineering experience within at least one major cloud data platform (Snowflake, Databricks, or Azure Synapse).
• Data Quality Engineering: Experience implementing automated validation checks to prevent dirty data from corrupting reporting layers.

Optional / Nice-to-Have Skillset:

• Experience extracting data from core transaction systems, specialized logistics systems (WMS), or cloud ERP APIs (NetSuite).
• Experience building or consuming standardized API Gateways for middleware integrations.
• Experience working with real-time or streaming data ingestion (Kafka, Azure Event Hubs).
• Familiarity with CI/CD deployment pipelines for data infrastructure.

The Data Engineer will handle ground-level technical execution, building and maintaining robust ETL/ELT pipelines to feed the newly defined data warehouse. This role focuses on the tactical ingestion, cleaning, and optimization of data streams coming from disparate legacy systems (WMS, ERP, CRM) to ensure data quality and predictive agility.