Data Engineer (with databricks)

Key Responsibilities

• Design, develop, and maintain ETL/ELT pipelines for ingesting, transforming, and loading data from multiple sources (operational systems, APIs, files, etc.).
• Collaborate closely with business users and domain experts (Finance, Operations, Procurement, Marketing, etc.) to understand business processes and translate them into data models.
• Implement robust data architectures supporting Data Warehouse and Data Lakehouse environments.
• Develop data transformation logic using SQL, Python, and Spark.
• Ensure data quality, consistency, and performance through monitoring, documentation, and automation.
• Work with BI, Data Science, and Data Governance teams to support analytics, reporting, and machine learning initiatives.
• Continuously improve data engineering best practices and development standards (CI/CD, DataOps).

 
Requirements

• 3+ years of experience as a Data Engineer or in a similar data-focused role.
• Proven experience with Databricks, Apache Spark or equivalent distributed processing platforms.
• Strong SQL skills and hands-on experience in building scalable data pipelines (ETL/ELT).
• Proficiency in Python
• Experience with relational and non-relational databases.
• Strong analytical and problem-solving skills with attention to detail.
• Excellent communication skills and the ability to collaborate effectively with non-technical stakeholders.
• Good understanding of business processes (Finance, Supply Chain, Operations, etc.) and their data flows.

 
Nice to Have

• Experience with Databricks Lakehouse, including Delta Lake, Unity Catalog, and Workflows.
• Familiarity with Airflow, Data Factory, or similar orchestration tools.
• Understanding of Data Governance, Metadata Management, and Data Modeling (Star Schema, Data Vault).
• Cloud certifications (e.g., Databricks Certified Data Engineer, Azure/AWS/GCP Data Engineer) – a strong advantage.