About the Role
We're seeking an experienced Data Engineer to design, build, and optimize scalable data pipelines within a modern cloud data platform. This is a hands-on engineering role focused on developing enterprise data solutions, supporting cloud modernization initiatives, and delivering high-quality datasets for analytics and reporting.

Key Responsibilities
Design and develop scalable ETL/ELT pipelines using Databricks, PySpark, and Spark.
Build and maintain cloud-based data platforms following modern lakehouse best practices.
Develop and optimize data ingestion, transformation, and processing workflows.
Integrate data from APIs, databases, cloud storage, and enterprise applications.
Support cloud migration and data modernization initiatives.
Improve pipeline performance, scalability, reliability, and cost efficiency.
Implement data quality, monitoring, governance, and CI/CD best practices.
Collaborate with technical and business stakeholders to deliver reliable data solutions.

Required Skills
5+ years of hands-on Databricks experience.
Strong experience with PySpark, Apache Spark, SQL, and Delta Lake.
Experience building enterprise ETL/ELT pipelines.
Experience working within Azure-based data platforms.
Experience integrating data from APIs and enterprise systems.
Familiarity with Git-based development and CI/CD processes.
Strong understanding of data modeling, data architecture, and cloud data engineering.

Lead Data Engineer Role

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